# Psychometric Study of the Scale for the Assessment of Developmental Assets in the Neighborhood in a Sample of Chilean Adolescents

^{1}Department of Psychology, Austral University of Chile, Valdivia, Chile^{2}Department of Social Work, University of La Frontera, Temuco, Chile^{3}Department of Psychology, Temuco Catholic University, Temuco, Chile

The Scale for the Assessment of Developmental Assets in the Neighborhood (SADAN) has shown acceptable psychometric properties for use in Spain and Chile. However, the original factor structure of five correlated factors and a second-order factor is not yet entirely clear. This study aimed to evaluate the scale’s psychometric properties of reliability and validity in a sample of Chilean adolescents. A cross-sectional design was used, with a sample of 2638 students (female = 49.1%) with an average age of 15.79 years (*SD* = 1.35). The results obtained when evaluating different confirmatory factor models show that the best structure is that of five correlated factors. We carry out a multigroup factor analysis up to the level of scalar invariance. We applied this analysis to the following groups: sex, type of school, and age. We conclude that the original version of the scale can be used in the Chilean context with slight modifications as it is necessary to deepen the validity evidence with external criteria.

## Introduction

An attractive frame of reference for understanding adolescence is the Positive Youth Development (PYD) approach (Benson et al., 2006; Oliva et al., 2008). Palomar-Lever and Victorio-Estrada (2014) understood adolescence as a stage of high plasticity, in which the relationships established with others and with the environment are fundamental aspects, for example, the neighborhood (Gálvez-Nieto et al., 2018).

Adolescents who have a positive perception of neighborhood security and social cohesion between neighbors show better psychological adjustment (Snedker and Herting, 2013; Mmari et al., 2014; Elliott et al., 2016; Vilhjalmsdottir et al., 2016). They also show higher life satisfaction (Maurizi et al., 2013; Rudolph et al., 2014; Rabinowitz et al., 2016), higher levels of prosocial behavior (Lenzi et al., 2013; Drinkard, 2014), a lower prevalence of problematic substance use (Tucker et al., 2013; Fuentes et al., 2015), and fewer disruptive behaviors (Chilenski, 2011).

There are psychometric tests to assess the perception that residents have of their neighborhood (Oliva et al., 2012a) even though most of these tests do not consider favorable aspects of adolescent development and adjustment (Herrero and Gracia, 2007). For this reason, the Scale for the Assessment of Developmental Assets in the Neighborhood (SADAN) provides an alternative to evaluate the perception of adolescents regarding different resources in the neighborhood that favor adolescent well-being.

Scale for the Assessment of Developmental Assets in the Neighborhood was developed from the positive youth development approach, and it assessed the social relationships established by adolescents in their neighborhood (Seligman and Csikszentmihalyi, 2000). It contributes to the psychological field as a measure for psychosocial evaluation in public health areas and promoting positive youth development.

Oliva et al. (2012a) developed and validated SADAN in Spain, analyzing the psychometric properties in a sample of 2400 adolescents from 17 educational centers. They perform an exploratory factor analysis using the Pearson matrix, the factorization method of the principal axis, and the oblimin rotation. They found a structure of five related factors that explained 62% of the variance. Subsequently, these authors carried out confirmatory factor analysis using the Pearson correlation matrix and the maximum likelihood estimation method. The evidence provided supported a second-order factor structure and five correlated factors. Oliva et al. (2012b) analyzed the relationships between some dimensions of SADAN and other factors, such as internalizing and externalizing problems, substance use, and life satisfaction. Subsequently, Gálvez-Nieto et al. (2019) carried out a study in Chile with a sample of 841 adolescents from 11 educational centers, which proposed a structure of five correlated factors. Considering that the structure is not clear in this population, the current study seeks to deepen the psychometric evidence in a larger sample.

Thus, the first hypothesis states that SADAN will maintain a second-order structure and five correlated factors. Furthermore, the scale scores will present adequate levels of reliability. The second hypothesis states that the scale will remain equivalent up to the level of scalar invariance for the variables sex, type of education, and age. The first objective of this study is to evaluate the psychometric properties of SADAN in a sample of Chilean adolescents. The second objective is to analyze the degree of invariance for the groups: sex, type of education, and age.

## Method

### Participants

We performed a stratified random sampling with a confidence interval of 95%, a variance of *p* = *q* = 0.50, and a margin of error of 3.30% (Scheaffer et al., 1987). As strata, we consider region, level of study (9th to 12th grade), and administrative dependency of the educational centers (public, private subsidized, and private). The sample (Table 1) corresponds to 2638 students of both sexes (50.9% male and 49.1% female) from 32 secondary schools, aged between 12 and 20 years (mean = 15.79 years, *SD* = 1.35). The students represent five zones in Chile: North (Antofagasta = four schools), North Central (Coquimbo = three schools), Central (Metropolitana = 16 schools), South Central (La Araucanía = seven schools), and South (Magallanes = two schools). The selected schools included students from various socioeconomic levels but mainly represented low and medium levels. Given the geographical characteristics of the country, we decided to exclude cities that contained less than 30% of the region’s population.

### Instruments

A sociodemographic questionnaire of multiple-choice questions was applied to document sex, age, date of birth, family origin (urban/rural), ethnic origin, grade level, and SADAN (Oliva et al., 2012a; Gálvez-Nieto et al., 2019).

Scale for the Assessment of Developmental Assets in the Neighborhood is a 22-item self-reporting instrument focused on the assessment of adolescents’ perception of their neighborhood. It uses an ordinal scale of seven points (1 = strong false, 7 = strong true). It is composed of five first-order factors: support and empowerment, attachment to the neighborhood, security, social control, and youth activities. It also uses a second-order factor that groups the five dimensions, called neighborhood assets.

### Procedure

To perform this study, we contacted the principals of the schools to request authorization to sample the students. To safeguard the ethical principles of the project, we gathered informed consent from students and parents or guardians. The research was approved and supported by the Scientific Ethics Committee at the Universidad de La Frontera. The instruments were applied voluntarily and anonymously during the first morning class.

### Data Analysis

We use confirmatory factor analysis (CFA) models to assess the factor structure of the scale. Following Asún et al. (2016), we used polychoric correlation matrix with the weighted least square with mean and variance adjusted (WLSMV) estimation method (Finney and Di Stefano, 2006). In this way, and considering the previous psychometric studies, in this study, we evaluate four models: one-dimensional, bifactor, second-order, and five correlated factors. We use the following several indices to analyze the goodness of the data fit by the different models. These indices are comparative fit index (CFI), Tucker–Lewis index (TLI), and root mean square error of approximation (RMSEA). In the case of the CFI and TLI indexes, values greater than or equal to 0.90 were considered a reasonable adjustment (Schumacher and Lomax, 1996), and values greater than 0.95 were considered a fine adjustment (Schreiber et al., 2006). For RMSEA, values less than 0.08 were considered a reasonable fit (Browne and Cudeck, 1993) and values less than 0.06 an excellent fit (Schreiber et al., 2006). Subsequently, we evaluate the degree of factorial invariance using the WLSMV estimation method for the groups (sex, type of school, and age). This analysis considers the following models (Vandenberg and Lance, 2000): M0 configural (equal number of factors), M1 metric (equal factor loadings), and M2 scalar (equality of intercepts). When we met the level of scalar invariance, we compared the latent means of the factors, setting those of the reference group to zero.

Finally, and based on the structure previously found, the reliability of each of the factors was determined using the following estimators: McDonald’s Omega and Cronbach’s alpha (Green and Yang, 2015; Trizano-Hermosilla and Alvarado, 2016). Additionally, following the recommendations of Domínguez-Lara and Merino-Soto (2015), the confidence intervals (CI) were estimated with the R 3.6 software for both Omega and Alpha using the “MBESS” (Kelly and Lai, 2019) and “Psych” (Revelle, 2020) packages, respectively.

## Results

### Descriptive Analysis

Table 2 presents the descriptive statistics of the items on the scale. When evaluating the means, we observed that item 13 obtained the highest score (*M* = 5.47, *SD* = 1.807). In contrast, item 22 presented the lowest mean (*M* = 2.86, *SD* = 1.816).

### Factor Structure Evaluation

To evaluate the factor structure, four CFA models were estimated with the 22 items on the scale. The first model tested was the one-dimensional model, which provided an unsatisfactory fit [WLSMV – χ^{2} (*df* = 209) = 33,037.671; CFI = 0.668; TLI = 0.634 RMSEA = 0.244 (C.I = 0.242–0.249)]. The second one tested was a bifactor model, and the results again showed unsatisfactory indices [WLSMV – χ2 (*df* = 187) = 4194.737; CFI = 0.960; TLI = 0.950 RMSEA = 0.090 (C.I = 0.08–0.093)]. The third model evaluated a second order structure that grouped five factors, and the results for this model were unsatisfactory [WLSMV – χ^{2} (*df* = 204) = 4902.601; CFI = 0.953; TLI = 0.946 RMSEA = 0.093 (C.I = 0.091–0.096)]. Finally, the estimation of a model of five correlated factors revealed an acceptable fit [WLSMV – χ^{2} (*df* = 199) = 3499.280; CFI = 0.967; TLI = 0.961 RMSEA = 0.079 (C.I = 0.077–0.082)]. From these results, it is possible to point out that the model that best fits the data is that of five correlated factors (Figure 1). As can be observed, all the factors of the scale present significant and positive correlations with values that range between a minimum of 0.060 for security and attachment to the neighborhood and a maximum of 0.802 between the dimensions of attachment to the neighborhood and support and empowerment.

### Factorial Invariance

Once the factorial structure of the scale was confirmed, we carried out factor invariance analysis for the groups sex, type of education, and age. As shown in Table 3, we evaluate the configuration invariance or M0, resulting in all satisfactory goodness-of-fit indices and statistically significant parameters. This result corroborated the structure of five correlated factors and the same items for the variables sex, type of education, and age.

Subsequently, the M1 metric invariance model was evaluated, adding restrictions to the factor loadings. The results indicate that there are no statistically significant differences between the configuration and metric models for the variables sex (*p*-ΔWLSMV - χ^{2} = 0.0164; ΔCFI = −0.001), type of education (*p*-ΔWLSMV - χ^{2} = 0.0496; ΔCFI = 0), and age (*p*-ΔWLSMV - χ^{2} = 0.0280; ΔCFI = 0).

Next, the level of M2 scalar invariance was analyzed, including constraints on the item intercepts to be equivalent in the groups. The results indicated that there are statistically significant differences between the metric and scalar invariance models for the variable sex (*p*-ΔWLSMV - χ^{2} < 0.001; ΔCFI = −0.002), type of education (*p*-ΔWLSMV - χ^{2} < 0.001; ΔCFI = −0.002), and age (*p*-ΔWLSMV - χ^{2} < 0.001; ΔCFI = −0.002). However, with the CFI difference criterion, the equality between both models is accepted.

Also, we evaluated the level of latent means between these groups. Results show statistically significant differences. In particular, females present the lowest levels for support and empowerment (−0.139, *p* < 0.001), attachment to the neighborhood (−0.219, *p* < 0.001), security (−0.102, *p* = 0.004), and youth activities (−0.208, *p* < 0.001). We did not observe statistical differences in the social control factor (*p* = 0.229).

For the type of education group, we also observed statistically significant differences in favor of scientific-humanistic schools in comparison to technical-professional schools for the factors of support and empowerment (−0.088, *p* = 0.008), security (−0.474, *p* < 0.001), and social control (−0.139, *p* < 0.001). There were no statistically significant differences in the factors of attachment to the neighborhood (*p* = 0.064) and youth activities (*p* = 0.146).

Finally, when comparing the latent means for the age group, statistically significant differences were observed in favor of students located in the age range between 12 and 15 years. Students aged between 16 and 19 years show the lowest levels in the latent dimensions of support and empowerment (−0.137, *p* < 0.001), attachment to the neighborhood (−0.132, *p* < 0.001), security (−0.220, *p* < 0.001), social control (−0.079, *p* = 0.001), and youth activities (−0.064, *p* = 0.038).

### Evidence of Reliability

For the latent factor, attachment to the neighborhood, reliability values were obtained with the omega coefficient of 0.932 (C.I = 0.926−0.938) and Cronbach’s alpha 0.931 (C.I = 0.927−0.935). For support and empowerment, we observed an omega coefficient of 0.903 (C.I = 0.895−0.909) and an alpha of 0.902 (C.I = 0.897−0.907). For security, we obtained an omega value of 0.840 (C.I = 0.828−0.851) and an alpha of 0.836 (C.I = 0.827−0.845). For the youth activities, the omega coefficient obtained was 0.786 (C.I = 0.770−0.801), and the alpha value was 0.784 (C.I = 0.772−0.796). Finally, for social control, the omega value was 0.736 (C.I = 0.716−0.755), and the alpha was 0.734 (C.I = 0.719−0.748). Therefore, we observed similar values for both reliability estimators as well as in their confidence intervals. Additionally, the tests showed adequate values of reliability for the five dimensions of the questionnaire.

## Discussion

This research had two objectives. The first objective was to evaluate the psychometric properties of SADAN in a sample of Chilean adolescents. The second was to analyze the degree of invariance for sex, type of education, and age. The results obtained show partial compliance with the first hypothesis, which stated that the scale scores would maintain a second-order structure and five correlated factors in addition to adequate levels of reliability. The second hypothesis had complete compliance and stated that the scale scores would be equivalent up to the level of scale invariance for the variables sex, type of education, and age.

The results of this study do not support the first hypothesis in the sense that the scale would maintain the second-order factor structure and five correlated factors. Instead, these results confirmed the presence of a structure of five correlated factors for the Chilean sample (Gálvez-Nieto et al., 2019).

It is interesting to note that, when the results of the original study were analyzed (Oliva et al., 2012a), it was observed that the second-order structure obtained a low loading factor for security (0.29), considering the factor loadings of the rest of the dimensions, support and empowerment (0.84), attachment to the neighborhood (0.70), social control (0.46), and youth activities (0.53), which are comparatively higher. The mismatch of the second-order model could be due to the questionnaire items being phrased negatively. Hence, we suggest that future psychometric studies modify the question phrasing.

The results of the invariance models show that SADAN applies to both males and females. However, latent trait levels differ significantly in the means of the following factors: support and empowerment, attachment to the neighborhood, security, and youth activities. In this study, we found that the latent means of these factors are higher in males, which is consistent with the findings obtained by the study by Gálvez-Nieto et al. (2019). These differences could be explained because adolescent females use and appropriate public space differently than males. In practical terms, females prefer safer spaces, showing similar behavior to adults supervising children and older adults (Pérez, 2012). Females also tend to perceive crimes of different kinds more frequently. They also have less control over threatening situations as well as a more significant number of experiences of victimization, both direct and indirect, increasing their perception of insecurity (Espinoza and Monge-Nájera, 2017; Valera-Pertegas and Guardia-Olmos, 2017).

The results of the factor invariance analysis in scientific-humanistic and technical-professional schools report that the SADAN applies for both school contexts. However, the latent means of the factors support and empowerment, security, and social control differ significantly, obtaining higher average scores from the students in scientific-humanistic schools. These differences can be explained because students who choose technical-professional schools present significantly lower socioeconomic levels. This condition is associated with neighborhoods with more critical problems of security, support and empowerment, and social control (Bellei et al., 2017).

The factor invariance analysis applied according to the group age of the students showed equivalence up to the level of scalar invariance. It shows significant differences in the levels of the latent means of the five factors of the instrument, obtaining higher averages for the students belonging to the younger age group. These results coincide with studies that evaluate psychosocial variables among students of different ages, who state that younger students tend to report a better assessment of the characteristics of their neighborhood (Gálvez-Nieto et al., 2020).

Regarding the limitations of the present study, we suggest continuing to expand the line of research to include other measures to assess the convergent and divergent validity of SADAN. On the other hand, the results of this study should be interpreted with caution because only a self-reporting instrument was used as an indicator. We feel that it would be interesting to evaluate not only the perception of the students but also the perspective of the parents or guardians of the adolescents. Another limitation to consider when interpreting the results is the overrepresentation of the public schools, which participated in a higher number than the other types of educational schools.

Future lines of research should analyze the invariance of the construct in other Latin American student populations.

## Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

## Ethics Statement

The studies involving human participants were reviewed and approved by the Comité de Ética de la Universidad de La Frontera. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

## Author Contributions

DV-B conducted bibliographic search, theoretical framework, integrated results, and contributed to the discussion. JG-N performed the data collection, methodological design, contributed to analysis, results, and discussion. IT-H contributed to the methodological design, performed the data analysis, and generated the results. SS-G review of the analysis of the invariance models and general review of the manuscript. KP conducted bibliographic search, theoretical framework, and contributed to the discussion.

## Funding

This work was produced within the framework of FONDECYT Project Nos. 1190844 and 11170522.

## Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## References

Asún, R. A., Rdz-Navarro, K., and Alvarado, J. M. (2016). Developing multidimensional likert scales using item factor analysis: the case of four-point items. *Sociol. Methods Res.* 45, 109–133. doi: 10.1177/0049124114566716

Bellei, C., Canales, M., Orellana, V., and Contreras, M. (2017). Elección de escuela en sectores populares: estado, mercado e integración social. *Rev. Austral. Ciencias Soc.* 31, 95–110. doi: 10.4206/rev.austral.cienc.soc.2016.n31-06

Benson, P., Scales, P., Hamilton, S., and Sesman, A. (2006). “Positive youth development: theory, research and applications,” in *Theoretical Models of Human Development. Volume 1 of Handbook of Child Psychology*, 6th Edn, ed. R. M. Lerner (Hoboken, NJ: Wiley), 894–941.

Browne, M., and Cudeck, R. (1993). “Alternative ways of assessing model fit,” in *Testing Structural Equation Models*, eds K. Bollen and J. Long (Beverly Hills, CA: Sage), 136–162.

Chilenski, S. M. (2011). From the macro to the micro: a geographic examination of the community context and early adolescent problem behaviors. *Am. J. Commun. Psychol.* 48, 352–364.

Domínguez-Lara, S., and Merino-Soto, C. (2015). ?Por qué es importante reportar los intervalos de confianza del coeficiente alfa de Cronbach?. *Rev. Latinoamericana Ciencias Soc. Niñez Juventud* 13, 1326–1328.

Drinkard, A. M. (2014). Predicting prosociality among urban adolescents: individual, family, and neighborhood influences. *Youth Soc.* 49, 528–547. doi: 10.1177/0044118X14543266

Elliott, M. C., Leventhal, T., Shuey, E. A., Lynch, A. D., and Coley, R. L. (2016). The home and the ‘hood: associations between housing and neighborhood contexts and adolescent functioning. *J. Res. Adolesc.* 26, 194–206. doi: 10.1111/jora.12183

Espinoza, J., Méndez, V., and Monge-Nájera, J. (2017). Percepción de seguridad, uso y mantenimiento de los parques municipales en Costa Rica, según el sexo de los usuarios. *Cuadernos Investig. UNED* 9, 65–71.

Finney, S., and Di Stefano, C. (2006). “Non normal and categorical data in structural equation models,” in *A Second Course in Structural Equation Modeling*, eds G. Hancock and R. Mueller (Greenwich, CT: Information Age), 269–314.

Fuentes, M. C., Alarcón, A., García, F., and Gracia, E. (2015). Consumo de alcohol, tabaco, cannabis y otras drogas en la adolescencia: efectos de la familia y peligro del barrio. *An. Psicol.* 31, 1000–1007.

Gálvez-Nieto, J. L., García, J. A., Vera-Bachmann, D., Trizano-Hermosilla, I., and Polanco, K. (2020). Multilevel latent class cluster analysis of school climate: individual, family and community factors. *Rev. Psicodidáctica* (in press). doi: 10.1016/j.psicod.2020.01.001

Gálvez-Nieto, J. L., Trizano-Hermosilla, I., Alvarado, J., Tereucan, J., and Polanco, K. (2019). Adaptación y validación de la escala de evaluación de los activos del barrio en una muestra de adolescentes chilenos. *Rev. Mexic. Psicol.* 36, 119–131.

Gálvez-Nieto, J. L., Vera-Bachmann, D., Trizano-Hermosilla, I., Polanco, K., and Salvo, S. (2018). Propiedades psicomeìtricas de la versioìn reducida de la Escala de Valores para el Desarrollo Positivo Adolescente (evdpa-r) en estudiantes chilenos. *Rev. Colomb. Psicol.* 27, 69–84. doi: 10.15446/rcp.v27n2.65500

Green, S. B., and Yang, Y. (2015). Evaluation of dimensionality in the assessment of internal consistency reliability: coefficient alpha and omega coefficients. *Educ. Meas. Issues Pract.* 34, 14–20. doi: 10.1111/emip.12100

Herrero, J., and Gracia, E. (2007). Measuring perceived community support : factorial structure, longitudinal invariance, and predictive validity of the PCSQ (perceived community support questionnaire). *J. Commun. Psychol.* 35, 197–217.

Lenzi, M., Vieno, A., Pastore, M., and Santinello, M. (2013). Neighborhood social connectedness and adolescent civic engagement: an integrative model. *J. Environ. Psychol.* 34, 45–54. doi: 10.1016/j.jenvp.2012.12.003

Maurizi, L. K., Ceballo, R., Epstein-Ngo, Q., and Cortina, K. S. (2013). Does neighborhood belonging matter? Examining school and neighborhood belonging as protective factors for Latino adolescents. *Am. J. Orthopsychiatry* 83, 323–334. doi: 10.1111/ajop.12017

Mmari, K., Lantos, H., Blum, R., Brahmbhatt, H., Sangowawa, A., Yu, C., et al. (2014). A Global Study on the Influence of Neighborhood Contextual Factors on Adolescent Health. *J. Adolesc. Health* 55, 13–20.

Oliva, A., Antolín, A., and López, A. M. (2012a). Development and validation of a scale for the measurement of adolescents’ developmental assets in the neighborhood. *Soc. Indic. Res.* 106, 563–576. doi: 10.1007/s11205-011-9822-9

Oliva, A., Antolín, L., Estévez, R. M., and Pascual, D. M. (2012b). Activos del barrio y ajuste adolescente. *Psychosoc. Intervent.* 21, 17–27. doi: 10.5093/in2012v21n1a1

Oliva, A., Hernando, A., Parra, A., Pertegal, M. A., Riìos, M., and Antoliìn, L. (2008). *La Promocioìn Del Desarrollo Adolescente: Recursos Y Estrategias De Intervencioìn.* Sevilla: Consejeriìa de Salud, Junta de Andaluciìa.

Palomar-Lever, J., and Victorio-Estrada, A. (2014). De- terminants of subjective well-being in adolescent children of recipients of the opportunities human development program in Mexico. *Soc. Indic. Res.* 118, 103–124.

Pérez, F. (2012). Diferencias entre los usuarios de seis parques públicos en Barcelona según el nivel de seguridad percibida en el barrio. Athenea Digital. *Rev. Pensamiento Investig. Soc.* 12, 55–66.

Rabinowitz, J. A., Drabick, D. A., and Reynolds, M. D. (2016). Youth withdrawal moderates the relationhips between neighborhood factors and internalizing symptoms in adolescence. *J. Youth Adolesc.* 45, 427–439. doi: 10.1007/s10964-015-0324-y

Revelle, W. (2020). *Psych**: Procedures for Personality, Psychometric, and Psychological Research.* Available online at: https://cran.r-project.org/web/packages/psych/psych.pdf (accessed March 9, 2020).

Rudolph, K. E., Stuart, E. A., Glass, T. A., and Merikangas, K. R. (2014). Neighborhood disadvantage in context: the influence of urbanicity on the association between neighborhood disadvantage and adolescent emotional disorders. *Soc. Psychiatry Psychiatr. Epidemiol.* 49, 467–475. doi: 10.1007/s00127-013-0725-8

Scheaffer, R. L., Mendenhall, W., and Ott, L. (1987). *Elementos de muestreo (G. Rendón and J. Gómez, Trans.).* México: Grupo Editorial Iberoamérica.

Schreiber, J., Nora, A., Stage, F., Barlow, E., and King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: a review. *J. Educ. Res.* 99, 323–337. doi: 10.3200/JOER.99.6.323-338

Schumacher, R., and Lomax, R. (1996). *A Beginner’s Guide to Structural Equation Modeling.* New Jersey: Lawrence Erlbaum Associates.

Seligman, M., and Csikszentmihalyi, M. (2000). Positive psychology: an introduction. Am. Psychol. 55, 5–14. doi: 10.1037/0003-066X.56.1.89

Snedker, K. A., and Herting, J. (2013). Adolescent mental health: neighborhood stress and emotional distress. *Youth Soc.* 4, 1–25. doi: 10.1177/0044118X13512335

Trizano-Hermosilla, I., and Alvarado, J. M. (2016). Best alternatives to cronbach’s alpha reliability in realistic conditions: congeneric and asymmetrical measurements. *Front. Psychol.* 7:769. doi: 10.3389/fpsyg.2016.00769

Tucker, J. S., Pollard, M. S., de la Haye, K., Kennedy, D. P., and Green, H. D. Jr. (2013). Neighborhood characteristics and the initiation of marijuana use and binge drinking. *Drug Alcohol Depend.* 128, 83–89. doi: 10.1016/j.drugalcdep.2012.08.006

Valera-Pertegas, S., and Guardia-Olmos, J. (2017). Vulnerability and perceived insecurity in the public spaces of Barcelona / Vulnerabilidad y percepción de inseguridad en el espacio público de la ciudad de Barcelona. *Psyecology* 8, 177–204. doi: 10.1080/21711976.2017.1304880

Vandenberg, R., and Lance, C. (2000). A review and synthesis of the measurement invariance literature: suggestions, practices, and recommendations for organizational research. *Organ. Res. Methods* 3, 4–70. doi: 10.1177/109442810031002

Keywords: adolescence, neighborhood, positive development, validity, reliability

Citation: Vera-Bachmann D, Gálvez-Nieto JL, Trizano-Hermosilla I, Salvo-Garrido S and Polanco K (2020) Psychometric Study of the Scale for the Assessment of Developmental Assets in the Neighborhood in a Sample of Chilean Adolescents. *Front. Psychol.* 11:972. doi: 10.3389/fpsyg.2020.00972

Received: 13 December 2019; Accepted: 20 April 2020;

Published: 12 June 2020.

Edited by:

Cesar Merino-Soto, University of San Martín de Porres, PeruReviewed by:

Carolina Gonzálvez, University of Alicante, SpainJeanette M. López-Walle, Autonomous University of Nuevo León, Mexico

Copyright © 2020 Vera-Bachmann, Gálvez-Nieto, Trizano-Hermosilla, Salvo-Garrido and Polanco. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: José L. Gálvez-Nieto, jose.galvez@ufrontera.cl