Adolescence is the stage of the vital cycle characterized by biological maturation processes and the psychological, cognitive, and social developments that allow the passage from childhood to adulthood (^{Gaete, 2015}). One of the main characteristics of this stage is the search for one’s identity, the motivation to belong to a group of peers and a shift to independence from the family unit. Multiple changes occur in different areas of life during this normal developmental process, so it becomes essential to measure the healthy aspects available to adolescents.

Psychological resources are dominant factors because they are attributes that help improve an individual’s performance skills and the way they adapt in different life situations, given that through them people are able to regulate themselves and their relationship with the environment (^{Alvaro et al., 2010}).

In this regard, the psychological unfolding processes are activated depending on each person’s abilities to manage high stress situations, as explained by ^{Hobfoll’s (1989}) theory of Conservation of Resources. This theory establishes that the psychological resources are utilized in the case of a perceived threat, as well as for a perceived loss of resources, as real loss constitutes a source of stress. The environment is constantly testing individuals as they are pushed to regulate, adapt, and adjust. The resources are used to confront high stress situations and to pursue desired goals; they are positive aspects of the personality. According to ^{Hobfoll’s (2002)} theory, the activation of the resources allows for a better adaptation to the environment; on the other hand, these resources are not independent: they cause a chain activation, meaning the stimulation of a resource triggers the activation of the others. Multiple studies back this theory (^{González, Andrade & Jiménez, 1997}; ^{Rueda & Pérez, 2005}; ^{Vázquez, Rivera & Quintanilla, 2011}).

Following this line of thought, the Positive Psychological Functioning model (PPF) offers a relevant perspective and theoretical framework to understand the individual differences regarding psychological resources during both the diagnostic evaluation and the therapeutic process, given that the psychological resources can be evaluated and trained.

From a conceptual point of view, the PPF’s background can be linked to the eudaimonic well-being perspective and the theory of Conservation of Resources.

Well-being can be conceptualized based on two aspects: subjective or hedonic, related to happiness, and psychological or eudaimonic, whose theoretical development is focused on people’s potential and ability to adjust to the environment (^{Muratori, Zubieta, Ubillos, González, & Bobowik, 2015}). In this regard, Robert Cumminis, renowned author in the area, developed the theory of subjective well-being homeostasis using the concept of homeostasis to represent the management of the state of well-being. He conceptualizes that there is a general disposition in subjects to maintain their subjective levels of well-being within a certain threshold (^{Tomyn, Weinberg, & Cummins, 2015}). This conceptualization states that, when homeostasis fails due to the presence of a major negative challenge, people experience a negative affect that may become a chronic condition if it lasts in time. However, if the external demands are intense, they may affect homeostasis; whether these levels off or becomes chronic depends on the personal resources available to the individual to adapt to the circumstances (^{Tomyn et al., 2015}).

On the other hand, the PPF model was developed based on the theory of Conservation of Resources, which defines psychological resources as personality traits that are valuable by their very nature because they are linked to achieving positive results. They favor the attainment of achievements and goals by enabling individuals to adapt to their environment; and finally, they are stable and can be learned (^{Hobfoll, 1989}). According to this author, these resources are usually linked, forming clusters in such a way that obtaining a resource enables the activation of another. The same occurs when a resource is lost (a concept he denominated resource caravans).

Carol Ryff, on her part, developed a multidimensional model for Psychological Well-being that is composed of the following resources: Self-acceptance, Positive Relations with Others, Autonomy, Environmental Mastery, Purpose in Life, and Personal Growth (^{Ryff, 1989}). This model is in line with the one previously mentioned, as it also proposes the resources are linked.

The presence or the development of psychological resources contributes to an individual’s optimal functioning, allowing them to confront internal and external situations, to increase individual, family, and social strength and, ultimately, to achieve well-being from an eudaimonic perspective (^{Padrós & Rivera, 2014}).

The Positive Psychological Functioning construct emerged from a recent research by the Universidad Complutense de Madrid which consisted of two studies. The aim of the first study was to describe how the psychological resources are organized (autonomy, resilience, self-esteem, purpose in life, enjoyment, optimism, curiosity, creativity, sense of humor, environmental mastery and vitality). The second study sought to replicate the results achieved in the first and to test the scale. The results indicated that there exists an interconnection between the psychological resources, which make up a second order construct they denominated PPF (^{Merino & Privado, 2015}).

The PPF is defined as “a molecule consisting of a set of interconnected atoms (psychological resources) whose contribution to the molecule varies depending on its magnitude (factor loadings)” (^{Merino & Privado, 2015}, p. 52). Thus, the concept links psychological functioning, the resources, the strengths and weaknesses, as well as the tendency of the magnitude of the factors. In consequence, the PPF is a second order construct of eudaimonic well-being, giving the idea of a dispositional tendency of positive psychological functioning, in accordance with the factorial weight of the psychological resource factors. Therefore, the definition of the PPF would be the positive functioning disposition of a person, which is composed of the mobility of the positive factors that integrate it, being activated by the strengths or weaknesses of the resources that compose it.

The scale that evaluates the PPF is composed of 33 items that are answered using a Likert-type scale from 1 to 5, where 1 is strongly disagree and 5 is strongly agree. The dimensions of the scale are composed as follows: self-esteem: items 1, 18, 20; resilience: items 2, 14, 25; optimism: items 3, 15, 21; creativity: items 4, 9, 12; autonomy: 5, 8, 10; environmental mastery: 6, 22, 31; vitality: 7, 19, 32; purpose in Life: 11, 24, 26; sense of humor: 13, 23, 29; enjoyment: 16, 28, 33; curiosity: 17, 27, 30 (item 22 is inverted).

The original data reported by the authors are that the second order factor loading on the first order factors is >.74 and the first order factor loading on the elements is >.60. The factor loadings proved to be statistically significant (p < .001). The RMSEA value was .06, indicating a good adjustment to the proposed factorial structure. The incremental fit index shows a moderate adjustment, the NFI value was .702 and CFI was .878. The parsimony-adjusted index presented values higher than .50, PNFI .644, showing a good adjustment of the data to the model (^{Merino & Privado, 2015}).

Subsequently, a research was carried out in Mexico City to validate the PPF scale. Data indicate that the reliability of the test is adequate, with a Cronbach's alpha of .70. The least reliable subscale is resilience (.56). The total score of the scale (including said subscale) showed adequate reliability (.91). The obtained NFI was .88 and the CFI was .92, meaning the model showed moderate adjustment (^{Merino, Privado, & Gracia, 2015}).

In addition, a study was published on the adaptation and validation of the scale for a Portuguese university population. It was composed of 1131 university students with an average age of 19.6. It reported good PPF psychometric properties, where most of the subscales had a Cronbach's alpha of >.60 and the total of the scale had an α of .90, concluding that the scale presents reliable levels for its use on the target population (^{Oliveira, Merino, Privado, & Almeida, 2017}).

A PPF scale validation study was recently published in Mexico with a sample of 313 adolescents with an average age of 12.7. Said research showed a 4-factor structure that, as a whole, explains 56.83 % of the total variance. Factor 1 was composed of 10 items corresponding to the self-esteem and resilience dimensions. Factor 2 included the enjoyment dimension, composed of 8 items. In the case of factor 3, 6 items were incorporated from the optimism dimension and factor 5 had 5 items from the autonomy-vitality dimension. 29 out of the 33 original items were used, while items 8, 9, 10 and 22 were eliminated (^{González, Torres, González, & Ruiz, 2018}).

The published validation studies, both the original and the ones carried out in the cities of Mexico and Portugal, which maintain the 11-factor scale distribution, all studied a university population; meanwhile, the last study, which presented a different factorial structure (of 4 factors), used a sample of adolescents, resembling the present instrumental study.

Results

Firstly, the items’ descriptive data were analyzed (see Table 1). Bartlett's test of sphericity and Kaiser-Meyer-Olkin measure of sampling adequacy were performed to determine if the items are sufficiently correlated. Bartlett's test had a significant index, (X2= 3073,7; gl= 528; p< .000) which rejects the null hypothesis, while the KMO index was .898, which indicates a very good adequacy level (^{Llorente, Ferreres, Hernández, & Tomás, 2014}). This confirms the relevance of using the scale for factor analysis.

Table 1: Descriptive statistics of the ítems 

To assess the validity of the scale it was necessary to carry out an exploratory factor analysis using the Unweighted Least Squares method (^{Pérez, 2004}). This estimation method is recommended for small samples, as it recovers the weak factor in the cases where the maximum likelihood method fails, and the model includes a higher number of factors (^{Ximénez & García, 2005}). The results put forward a seven-factor solution that explains 72.91 % of the variance. It is worth noting that other studies used different methods and they all yielded a seven-dimension solution (see Table 2).

Table 2: Factorial structure of the scale 

^Note: Extraction method: unweighted least squares. Method of rotation: Equamax with Kaiser normalization.

A parallel analysis was carried out based on the factor’s minimum range (^{Timmerman & Lorenzo Seva, 2011}), which reports a single second order dimension, in accordance with the original analyses of the scale. The scree plot confirms the second order structure (see Figure 1).

Figure1: Scree Plot of PPF scale 

The following is a description and interpretation of the emerging factors with an analysis of the obtained results taking into account the involved constructs theory (^{Martínez-Arias, Hernández-Lloreda, & Hernández-Lloreda, 2006}). The seven emerging factors in the sample are grouped as follows: F1 (self-esteem) items 1-5-18-20; F2 (autonomy) items 8-10-12; F3 (purpose in life) items 2-11-14-16-24-25-26; F4 (optimism/enjoyment) items 3-7-15-19-21-32-33; F5 (curiosity) items 4-9-17-27-28-30; F6 (sense of humor) items 13-23-29; F7 (environmental mastery) items 6-22-31.

Moreover, the Cronbach’s α values for the total of participants for each of the seven emerging factors were: Factor 1 corresponds to Self-esteem (α .84), Factor 2 Autonomy (α .87), Factor 3 Purpose in Life (α .82); Factor 4 Optimism/Enjoyment (α .82), Factor 5 Curiosity (α .83), Factor 6 Sense of Humor (α .83) and Factor 7 Environmental Mastery (α .87). Table 3

Table 3: Factor correlation matrix 

Descriptive statistics (mean, standard deviation, minimum, maximum, skewness and kurtosis) of the instrument’s 7 resulting factors (see Table 4) were analyzed.

Table 4: Descriptive statistics for the sample of adolescents 

As can be seen, these results suggest adequate values for the emerging factors. Lastly, the calculations for scale reliability resulted in a Cronbach’s Alpha of .93 which is considered a very acceptable index.

The scale adjustment indexes for this seven-dimension item grouping model according to their factor loading reached acceptable values: χ2=5222.96, p= .00, CFI= .991, TLI= .97, y RMSEA= .09, with an adequate item factor loading for the found factors. This factorial solution explains the 62.15 % variance, which is considered satisfactory given that the minimum reference for social sciences is 60 % of the total variance (^{Hair, Anderson, Tathan, & Black, 2004}).