Emotional intelligence, motivation and learning strategies: the SSREI and MSLQ-SF questionnaires

Intangible Capital IC, 2025 – 21(1): 131-149 – Online ISSN: 1697-9818 – Print ISSN: 2014-3214 https://doi.org/10.3926/ic.2714 Emotional intelligence, motivation and learning strategies: The SSREI and MSLQ-SF questionnaires Isabel Coronado-Maldonado* , Rocío Díaz-Muñoz , José Luis González-Sodis Universidad de Málaga (Spain) *Corresponding author: [email protected] r[email protected], [email protected] Received March, 2024 Accepted January, 2025 Abstract Purpose: The influence of a good management of Emotional Intelligence (EI) on personal attitude shows a motivational impact that allows better results in any field, work, social or personal. This study analyzes EI and Motivation (MO) through the learning strategies present in 402 university students of the Faculty of Economics and Business and the Faculty of Marketing and Management. It also analyzes the impact of gender on these results and identifies the dimensions of the variables studied. Design/methodology/approach: For this purpose, we applied the motivational tools Motivated Strategies for Learning Questionnaire (MSLQ) and the Self-Report Emotional Intelligence Test (SSEIT). Findings: Based on the identification of five IE and four MO dimensions, the results of this study indicate high levels of active participation and interest in the subject. Students show a desire to learn effectively and for their learning to be of quality, motivating and useful for understanding the world around them and applying that knowledge to improve it. In relation to the impact of the gender factor, no significant differences or attitudinal patterns were found. Responses and behaviors were homogeneous for both genders. Originality/value: This work identifies the dimensions of EI and MO of university students in terms of involvement, interest in the subject of study and use of learning tools. Confirming that these parameters influence academic performance, the results allow us to determine in which aspects it is necessary or advisable to work on. Attitudes, motivation and intelligent emotional management can provide a competitive advantage. Keywords: Emotional intelligence, Motivation, Learning strategies, Performance Jel Codes: I23, D91, O33 To cite this article: Coronado-Maldonado, I., Díaz-Muñoz, R., & González-Sodis, J.L. (2025). Emotional intelligence, motivation and learning strategies: The SSREI and MSLQ-SF questionnaires. Intangible Capital, 21(1), 131-149. https://doi.org/10.3926/ic.2714 -131- Intangible Capital – https://doi.org/10.3926/ic.2714 1. Introduction Emotional Intelligence (EI), is the ability to effectively manage one’s own and others’ emotions, facilitates learning by fostering self-efficacy and emotional resilience, Goleman (1995). On the other hand, Motivation can be understood as “the set of internal and external processes that activate, direct, and sustain behaviors related to learning”. Authors such as Ryan and Deci (2000) have explored this idea within the framework of self-determination theory, focusing on internal factors (intrinsic motivation, which arises from genuine interest and personal satisfaction in learning) and external factors (extrinsic motivation, related to external elements such as rewards or recognition) that influence behavior. In this way, EI, motivation, and learning strategies are interconnected factors that significantly influence academic performance. According to a study by Tang and He (2023), EI positively impacts learning motivation, particularly in high-stress contexts such as the COVID-19 pandemic. Moreover, self-efficacy and social support serve as key mediators in this relationship, enabling students to develop more effective strategies to maintain their engagement with studies. Additionally, research on hybrid learning environments, which combine in-person and virtual classes, has shown that EI not only enhances study habits but also increases cognitive engagement. This engagement acts as a bridge between EI and learning strategies, helping students better address challenges associated with rapid changes in teaching methods, such as the shift to online learning due to university closures (Iqbal, Asghar, Ashraf & Yi, 2022). Furthermore, EI has been found to correlate positively with academic performance and students’ ability to set clear goals and overcome obstacles. According to an analysis by Chang and Tsai (2022), EI enables students to integrate rationality and emotions, resulting in better academic decision-making and interpersonal relationships. Factors such as self-motivation and emotional regulation directly contribute to the implementation of more effective learning strategies. These studies emphasize the importance of incorporating EI development into educational programs, not only to improve academic outcomes but also to prepare students for broader emotional and social challenges. The present research analyses the importance of EI, motivation and learning strategies of students at university level. We also consider whether there are gender differences in this behaviour. Regarding the specific objectives of the study, we examine the degree of intraand interpersonal EI of these students, in addition to considering their level of motivation in terms of their involvement with, appreciation for and interest in the subject under study. In addition, we aim to identify the dimensions of these two variables, EI and motivation, by carrying out a factor analysis. To this end, we apply the motivation tools Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich, 1991), and the Self-Report Emotional Intelligence Test (SSEIT) (Schutte, Malouff, Hall, Haggerty, Cooper, Golden et al., 1998), which was originally carried out with students from the Faculty of Economics and Business Studies and the Faculty of Marketing and Management. The paper is divided into four parts, firstly, we mention the literature review, in it, the importance of the subject, the contribution of our work and the objectives set in it are reflected, while defining the theoretical framework, where the concept of emotional intelligence, motivation, academic performance and learning are introduced, as well as the different measures of evaluation of these variables. Likewise, we describe how these variables are related within the main existing research in the literature. Secondly, we show the methodology applied, thirdly, we provide the results extracted. Finally, fourthly, we formulate the conclusions and discussion and future lines of research. In short, in the review of the literature of the last ten years we would like to highlight, on the one hand, the article by Niroomand, Behjat and Rostampour (2014) where they relate EI, the SSREI and MSLQ-SF questionnaires jointly in Iranian students. According to our search in WOS, we found only this research linking the two. Regarding the research by Niroomand et al. (2014), in addition to the relationship of the three concepts, we have provided the possible gender difference among respondents at the University of Malaga (Spain). -132- Intangible Capital – https://doi.org/10.3926/ic.2714 On the other hand, we highlight the study of Tejani, Khan, Ejaz and Shamsy (2021) Pakistani students, where a marked gender difference in motivation and learning in motivation and learning strategies. It should be noted that our study is carried out in diverse ethnographic contexts, so we do not intend to make direct comparisons. The results of this study allow us to answer different questions related to the importance of emotional intelligence, motivation and learning strategies of university students: What is the level of EI and Motivation (MO) through learning strategies presented by university students? Are there significant differences according to gender? What are the dimensions of the EI and motivation variables? 2. Review of Literature 2.1. Emotional Intelligence Intelligence refers to intelligence quotient, which is considered to be the universal indicator of an individual (Goleman,1995). Thus, researchers such as Gardner (1983) and Sternberg (1988) have suggested that the term “intrapersonal and interpersonal intelligence” provided a basis for later models of emotional intelligence (EI). These authors suggested that the essence of intrapersonal intelligence is the ability to know one’s own emotions, while that of interpersonal intelligence is the ability to understand the emotions and intentions of other individuals (Gignac, Palmer, Manocha & Stough, 2005; Schutte et al. 1998). Subsequently, Salovey and Mayer (1990) proposed a broader approach to understanding intelligence. They were the first to introduce the term “emotional intelligence”, understood as the ability to perceive and express emotion in oneself (via verbal and non-verbal expression) and in others (via non-verbal perception and empathy), as well as to regulate emotion in oneself and in others, and understand how to use that emotion. Thus, some people are better able than others to process information about emotions and to use it to guide their thinking and behaviour. The concept of EI was gradually introduced into the scientific literature. It was popularised and expanded upon by Goleman (1995), who added a set of communicative and social skills influenced by the understanding and expression of emotions. Later, Bar-On (2000) defined EI as a set of knowledge and skills that affects the emotional and social influences on an individual’s ability to be aware. According to Bar-On (2000), these skills are non-cognitive and enable individuals to understand, be aware of, and know how to express and control their emotions in order to increase their success in life. Following introduction of the concept of EI, some studies have interpreted it as a set of mental skills, and others as an eclectic mix of positive traits, such as happiness, self-esteem and optimism (Mayer, Salovey & Caruso, 2008). Transferring the concept of EI to teaching, research concerning the quality of higher education has increased significantly, as noted by Froiland and Worrell (2016), Mortiboys (2013) and Rao and Sachs (1999). Authors such as Perera and Di Giacomo (2013) have proposed conceptual models that link trait EI directly and indirectly to educational performance in various educational environments. One such environment these authors considered was the university, wherein, at the moment of university transition, students have to face new challenges and experiences at the academic and social levels that can cause stress, at least in the first term of the course. Thus, higher academic performance is achieved by individuals who possess higher EI, which enables stronger and more enduring interpersonal relationships to form (Brackett, Rivers & Salovey, 2011), thus contributing to higher intellectual development in general (Berndt, 1999, as cited in Altwijri, Alotaibi, Alsaeed, Alsalim, Alatiq, Al-Sarheed et al., 2021; see also Ford & Smith, 2007). Similarly, in the study carried out by Kasemy, Kabbash, Desouky, Abd-El-Raouf, Aloshari and El-Sheikh (2022) student motivation was the highly significant predictor of academic performance followed by learning, emotional intelligence, and educational environment. Along these lines, in the research carried out by Tejani et al. (2021) with Pakistani students, significant correlations were found between eleven subscales of the MSLQ and the students’ academic performance Therefore, motivation has an impact on academic results and a considerable gender difference prevails in terms of motivation. and learning strategies. However, the model fit indices in SEM show a relative fit and poor fit in some of the indices. -133- Intangible Capital – https://doi.org/10.3926/ic.2714 For his part, Lei (2024), in his research work with Chinese students enrolled in science, technology, engineering and mathematics programs, in several universities in China, found a significant correlation between self-regulated learning, motivation to learn science and emotional intelligence, and thus, be able to improve academic results. Authors such as Marín-Marín, López-Belmonte, Lampropoulos and Moreno-Guerrero (2023) focused on knowing the impact of a reading plan (understood as a skill in the person) in a control and experimental group with students from different primary schools in Spain, in dimensions such as motivation, emotional intelligence, showing a positive effect in the improvement of these dimensions in the students in the experimental group. High EI also has a favourable impact on language development (Kourakou, 2018; Rostampour & Niroomand, 2013); specifically, language learning, emotional characteristics and cognitive ability are beneficial for reading comprehension, introspection, speaking and writing (Abdolrezapour & Tavakoli, 2012; Afshar & Rahimi, 2016; Asadollahfam, Salimi & Pashazadeh, 2012; Chang, 2021; Motallebzadeh, 2009). Moreover, as Aki (2006) stated, it is important for language learners to be emotionally intelligent rather than academically intelligent. Similarly, Niroomand et al. (2014) studied the relationship between EI and motivation among Iranian university students, finding that students of English as a foreign language learners plays a significant and determining role in expanding their language skills. However, this is in contrast to studies by Vali-Mohammadi and Bagheri (2011) and Pishghadam (2009), which did not show a positive relationship between the two variables, EI and second language learning as the results were discussed. Along the same lines, the study carried out on high school students by Nieto-Carracedo, Gómez-Iñiguez, Tamayo and Igartua (2024) indicated that emotional intelligence cannot be directly related to academic performance, although it can be related through mediating factors. Indirectly, emotionally intelligent students have higher levels of emotional well-being, which predicts better learning strategies and, in turn, is associated with academic performance. Also, Razavi, Omid, Rezaee and Khalesi (2020) suggested in their research that although there was a significant positive correlation between EI and motivated strategies, there was an insignificant poor correlation between EI and academic performance but a significant positive correlation between motivated strategies. In this way, it can be said that learning motivation is influenced by students’ EI, such that there is a positive relationship between students’ learning motivation (Chang & Tsai, 2022), EI (Dubey, 2012), language performance (Henter, 2014) and self-efficacy (Berenson, Boyles & Weaver, 2008; CussóCalabuig, Farran & Bosch-Capblanch, 2018; Nonis & Fenner, 2012; Yokoyama, 2019). Thus, in general, all these variables influence students’ academic performance. Similarly, a study by Altwijri et al. (2021) with Saudi Arabian medical students found a positive relationship between EI and academic success and that both are vital for increasing academic performance. Likewise, some studies have focused on the impact of EI in education and how learning motivation is related to students’ EI (Dubey, 2012) and academic performance (Duchatelet & Donche, 2019). Thus, students’ learning effectiveness is related to their motivation (Bain, McCallum, Bell, Cochran & Sawyer, 2010). Achieving good learning outcomes is difficult without motivation (Tella, 2007). Therefore, the effectiveness of student learning is related to student motivation (Bain et al., 2010). 2.2. Motivation, Academic Performance and Learning Numerous definitions related to motivation and its different forms can be found in the literature (e.g., Lepper, 1988; Pintrich & Garcia, 1993; Reeve, 1994; Schunk & Meece, 2006; Williams & Burden, 1999). Our study utilises the definition by Gardner (1985); that is, “motivation is a combination of effort plus the desire to achieve a goal plus favourable attitudes towards the goal to be achieved”. Thus, following Ramírez-Mauleón (2005), Rinuado, Chiecher and Donolo (2003), and Rinuado, de la Barrera and Donolo (2006), when it comes to understanding the academic performance of university students, motivation must be considered as a fundamental element, as it is highly relevant for the implementation of learning strategies (Martínez & Galán, 2000). Oxford (2003) defined learning strategies as “operations employed by the learner to assist in the acquisition, storage, retrieval and use of information, as well as specific actions taken by the learner to make learning easier, faster, more enjoyable, more self-directed, more effective and transferable to new situations”. -134- Intangible Capital – https://doi.org/10.3926/ic.2714 Jaramillo-Mediavilla, Basantes-Andrade, Cabezas-González and Casillas-Martín (2024) highlight in their systematic review that augmented reality enhances student motivation by making learning experiences more interactive and engaging. Similarly, the study by Amores-Valencia, Burgos and Branch-Bedoya (2022) demonstrates how augmented reality can facilitate active and autonomous learning, particularly in science and mathematics. In line with this, Calleros and Gastelú (2024) suggest that active learning strategies, such as gamification and flipped classes, can make learning research methodology more engaging and participatory. Additionally, Pinto-Santuber, Bravo-Molina, Ortiz-Salgado, Jiménez-Gallegos and Faouzi-Nadim (2023) argue that motivation, self-regulation of learning, and digital competence are key variables influencing academic performance and the willingness to learn in distance education contexts. Building on these perspectives, Acosta-Mejía, Velandia-Sacristán and Martínez-Álvarez (2022) emphasizes the critical role of teachers’ pedagogical strategies in developing competencies for more creative and effective learning. Furthermore, Valenzuela, Miranda-Ossandon, Muñoz, Precht, del Valle and Vergaño-Salazar (2024) explore how teaching practices shape motivation-oriented learning strategies by integrating theories such as Self-Determination and Expectancy-Value. Also, Libao, Sagun, Tamangan, Pattalitan, Dupa and Bautista (2016) analyze how various types of motivation impact academic performance in science learning, highlighting additional factors like self-efficacy and control of learning beliefs that also significantly influence academic outcomes. On the other hand, Pintrich (2000) defined self-regulated learning as “an active and constructive process by which students set goals for their learning and then attempt to monitor, regulate and control their cognition, motivation and behaviour, guided and constrained by their goals and by the contextual characteristics of the environment”. Thus, when approaching learning, different types of learners may emerge: some will approach learning superficially, some deeply and some strategically (Brown, 2004, as cited in Hasanzadeh & Shahmohamadi, 2011). Students who only memorise and reproduce content learn superficially; those who understand the subject matter and integrate the content and understand it learn deeply; and those who manage their time and work space efficiently, and learn structurally (consequently achieving better results), learn strategically. Furthermore, several studies in the scientific literature have shown the relationship posictive between learning, motivation and teaching (Hall, Sampasivam, Muis & Ranellucci, 2016; Thomas & Muller 2016). In the particular case of shocking situations such as the COVID-19 pandemic, where online courses had to be taken and adapted to, we note as Chang and Tsai, (2022), in a study of Chinese university students, that students with higher EI are likely to be more motivated to learn. Similarly, during the online courses these students were able to feel the emotions of others, impacting their self-efficacy and indirectly influencing their academic performance. 2.3. Instruments: MSLQ and SSREI There are various tools for studying motivation and its relationship with learning. For example, Castañeda (2004) and Sabogal, Barraza, Hernández and Zapata (2011) used the Inventory of Learning Styles and Motivational Orientation, which makes it possible to quickly and systematically identify the self-assessments that secondaryand higher-education students make about their learning style and their motivational orientation to study. Pintrich (1988) applied the MSLQ, which in its original version consisted of 81 items. This questionnaire was later modified (Pintrich & García, 1993), reduced to 40 items, and called the Motivation and Learning Strategies Questionnaire – Short Form (MSLQ-SF). Pintrich and García (1993) wanted to examine the ability of variables related to the use of learning strategies to explain the academic performance of students (Vásquez-Córdova, 2021), and showed that it is a reliable instrument. Roces, Tourón and González-Torres (1995) adapted the questionnaire to the Spanish context under the name Cuestionario de Estrategias de Aprendizaje y Motivación. The instrument has also been used by authors such as Curione and Huertas (2017), Duncan and McKeachie (2005), Dunn, Lo, Mulvenon and Sutcliffe (2012), Hilpert, Stempien, van der Hoeven-Kraft and Husman (2013), Radovan (2010), and Zurita-Ortega, Martinez-Martinez, Chacon-Cuberos and Ubago-Jiménez (2019), among many others. Both the SSREI (Self-Report Emotional Intelligence) and the MSLQ-SF have been used to investigate how emotional intelligence influences motivation and learning strategies in educational settings. Recent work has -135- Intangible Capital – https://doi.org/10.3926/ic.2714 illustrated that EI, measured through tools such as the SSREI, interacts with the MSLQ-SF motivation scales to predict academic outcomes Quílez-Robres, Usán, Lozano-Blasco and Salavera (2023). In the same way, research such as that of Gomes and Schmidt (2023) shows that EI can enhance the application of learning strategies and improve academic outcomes through tools like SSREI and MSLQ-SF. 3. Methodology In order to achieve the study objectives, a survey was developed focused on the quantification and systematic review of emotional and motivational traits. Two validated questionnaires were applied. The first was MSLQ-SF (Pintrich, 1991). This questionnaire consists of two parts: (a) motivation, which is further divided into two microvariables, value components and affective components; and (b) learning strategies, which is further divided into the microvariables cognitive and metacognitive strategies, resource management strategies and value components. The second questionnaire was the SSEIT (Schutte et al., 1998), which is based on Salovey and Mayers’ (1990) original theory of EI and consists of a 33-item self-report measure of students’ EI. The SSEIT items utilise a 5-point Likert scale. Factors identified via the SSEIT include perception of emotions (PE), managing emotions in oneself (MES), managing emotions of others and emotion utilisation (UE). These questionnaires were answered as mentioned above by students from different branches of the Faculty of Economics and Business Studies and the Faculty of Marketing and Management specified in Table 1. The participating population are students of the Faculty of Economics and Business between 18 and 30 years old. The class was previously informed that we were going to carry out the questionnaire deeply. A typical quantitative descriptive methodology study was carried out with 402 students (Málaga, Spain), from the aforementioned faculties, based on a survey carried out with their students. We analysed the results obtained for EI and motivation on the one hand, and motivation on the other. In both cases, Likert-type questionnaires (1 being the minimum score, 5 the maximum) were used. These were created using survey tools used on the university’s virtual campus (on line). Prior to answering the questionnaire, students were instructed on the considerations to be taken into account when answering the questionnaire. Data collection was carried out during the first four-month period of the academic year 2021-2022, with the questionnaires closing at the end of the year 2022. The time used to answer the questionnaires was similar for all students (around 35 minutes). Questionnaires were anonymous. Degree Frequency % Total Accumulated % Business Administration and Management 113 28.11 28.11 Economics and Business Administration and Management 14 3.48 31.59 Finance, Accounting and Business Administration and Management 135 33.58 65.17 Economics 19 4.73 69.9 Finance and Accounting 100 24.88 94.78 Marketing 21 5.22 100 Total 402 100 100 Table 1. Participants in each university degree Items Descriptors 1 I try to change the way I study to meet the requirements of the subject and the teacher’s teaching style. 2 Continued weekly readings and assignments for the course 3 In a mid-term exam I think about how poorly I am doing compared to others. 4 I relate what I read for class to what I know. 5 When I study the readings for this subject I underline the material to help me organise my thoughts. 6 Given a theory, interpretation or conclusion I determine its support in evidence (proof, examples). 7 If I am confused about what I read, I go back and try to sort it out -136- Intangible Capital – https://doi.org/10.3926/ic.2714 Items Descriptors 8 I usually study in a place where I can concentrate 9 I work hard academically even if I don’t like what I do. 10 I prefer subject material that arouses my curiosity even if it is difficult. 11 I think that the subject material is useful for learning. 12 Before any assessment I think about the consequences of failure 13 When studying, he summarised the main ideas, readings and concepts of the class. 14 When I study for subjects I review readings and class notes looking for main ideas. 15 I try to think through a topic and decide what I am supposed to learn. 16 I am generally interested in the topics of the subjects I am interested in. 17 Before studying new subject material I often review it to see how it is organised. 18 When studying for classes I set goals to direct my activities in each study period. 19 The most satisfying thing for me in this subject is to understand the content as well as possible. 20 I rarely find an hour to review my notes or readings before the exam. 21 I feel an uneasiness that disturbs me when I take an exam. 22 I try to understand the material in this class by making connections between the readings and the concepts given in the class. 23 When I study for the course I review my lecture notes and make an outline of the important concepts. 24 I try to relate my ideas to what I am learning in this subject. 25 When studying for this subject I try to determine which concepts I don’t understand well. 26 I find it difficult to adapt to a study Schedule. 27 When course materials are boring and uninteresting, I push myself to finish them. 28 Understanding the subject matter of this course is very important to me. 29 I feel my heart beating fast when I take a test. 30 I try to apply ideas from subject readings in other classroom activities such as presentations and discussions. 31 Whenever I read, hear or hear a statement or conclusion in this class I think of possible alternatives. 32 I question myself to make sure I understood the material I have been studying in this class. 33 I have a regular place to study. 34 In a class I like I prefer subject material that really challenges me so I can learn new things. 35 I am very interested in the area to which this subject belongs. 36 I use the course material as a starting point and try to develop my own ideas on it. 37 If the course materials are difficult to understand change the way you read it. 38 I make good use of my study time for this subject. 39 When the subject work is difficult, I give up and only study the easy stuff. 40 If I take messy notes in class, I make sure to sort them out later. Table 2. Motivation and Learning Strategies Questionnaire Short Form - MSQF SF. Pintrich (1991) Using Jamovi we proceeded to calculate the measures of central tendency, mean, mode and median, as well as the standard deviation, skewness and kurtosis for the variable Emotional Intelligence (EI) in all its dimensions these data are analysed in the results section. Also, we have calculated with the same procedure the measures of central tendency, mean, mode and median for the variable Motivation (MO) and all its dimensions, which are analysed in the results section. In both cases we calculated the Shapiro-Wilk test. The reliability, validity and test-retest reliability of both EI and MO were analysed to determine the possibility of conducting a factor analysis of both categories. Subsequently, an exploratory factor analysis was performed with Varimax principal extraction method using JAMOVI To assess the model fit, the Chi-square indices were used in -137- Intangible Capital – https://doi.org/10.3926/ic.2714 relation to their degrees of freedom, RMSEA, SRMR, for both EI and MO TLI and CFI (Kaplan, 2009; Hormigo, 2014; Shi, Maydeu-Olivares & DiStefano, 2018). Dimension Items Assessment of homework 20, 26, 39 Test anxiety 3, 12, 21, 29 Preparation strategies 4,5 22, 24, 25 Organisational strategies 13, 14, 23, 40 Critical thinking 1, 6, 15 Self-regulation of metacognition 16, 30, 31, 32, 34, 35, 36 Study time and habits 2, 18, 17, 18, 33, 38 Self-regulation of effort 7, 9, 11, 17, 19, 27, 28 Intrinsic goal orientation 10, 37 Table 3. Dimensions of Motivation. Pintrich (1991) Items Description of dimensions 1 I know when to speak about my personal problems to others. 2 When I am faced with obstacles, I remember times I faced similar obstacles and overcame them 3 I expect that I will do well on most things I try. 4 Other people find it easy to confide in me. 5 I find it hard to understand the non-verbal messages of other people. 6 Some of the major events of my life have led me to re-evaluate what is important and not important. 7 When my mood changes, I see new possibilities. 8 Emotions are one of the things that make my life worth living. 9 I am aware of my emotions as I experience them. 10 I expect good things to happen. 11 I like to share my emotions with others. 12 When I experience a positive emotion, I know how to make it last. 13 I arrange events others enjoy. 14 I seek out activities that make me happy. 15 I am aware of the non-verbal messages I send to others. 16 I present myself in a way that makes a good impression on others. 17 When I am in a positive mood, solving problems is easy for me. 18 By looking at their facial expressions, I recognize the emotions people are experiencing. 19 I know why my emotions change. 20 When I am in a positive mood, I am able to come up with new ideas. 21 I have control over my emotions. 22 I easily recognize my emotions as I experience them. 23 I motivate myself by imagining a good outcome to tasks I take on. 24 I compliment others when they have done something well. 25 I am aware of the non-verbal messages other people send. 26 When another person tells me about an important event in his or her life, I almost feel as though I have experienced this event myself. 27 When I feel a change in emotions, I tend to come up with new ideas. 28 When I am faced with a challenge, I give up because I believe I will fail. 29 I know what other people are feeling just by looking at them. -138- Intangible Capital – https://doi.org/10.3926/ic.2714 Items Description of dimensions 30 I help other people feel better when they are down. 31 I use good moods to help myself keep trying in the face of obstacles. 32 I can tell how people are feeling by listening to the tone of their voice. 33 It is difficult for me to understand why people feel the way they do. Table 4. Emotional Intelligence Items. Pintrich (1991) Dimension Items Assessment of my own emotions (self-assessment of my emotions) 9, 22 Valuing emotions in others 5, 15, 18, 25, 29, 32, 33 Emotional expression 1, 11 Emotional self-regulation 2, 3, 12, 14, 23, 28, 31, 10 Emotional regulation of others 4, 13, 16, 24, 30 Using emotions in problem solving 7, 17, 20, 27 Uncategorized 6, 8, 19, 21, 26 Table 5. Dimensions of EI. Schutte et al. (1998) We first analysed the EI questionnaire and then the motivation questionnaire, and conducted reliability and validity tests in order to verify whether factor analysis was appropriate in both cases. The instrument used to obtain the data was the MSLQ-SF scale of Pintrich (1991) and the SSREI of Schutte (1998), given that the aim was to identify aspects of EI and motivation. Subsequently, exploratory factor analysis was carried out with a varimax principal extraction method using JASP. To assess the model fit, chi-square indices were used in relation to their degrees of freedom, RMSEA, SRMR, TLI and CFI (Hormigo, 2014; Kaplan, 2009; Shi et al., 2018). 4. Results The results were determined via descriptive statistical analysis of the variables of interest. In parallel, the latent structure of the measurement was confirmed through exploratory factor analysis. We then calculated scores for the participants, according to the scales analysed, by averaging their scores on the items corresponding to these factors. The results obtained made it possible to determine, in a descriptive way, the different response profiles of the respondents, which revealed a range of attitudes with different frequencies. The Cronbach’s alpha score for EI was 0.86, while the McDonald’s omega was 0.87, (Table 10 and Table 11). Regarding motivation, the Cronbach’s alpha was 0.90 and McDonald’s omega was 0.91. Barlett’s test of sphericity in both cases yielded a result below 0.001. Based on the approach outlined above, we considered the EI and motivation for each, such that a series of factors or dimensions were identified and were determined to be consistent with the results gathered in the study. Figure 1 details the dimensions established. Regarding EI, the factor structure for the analysis was extracted by means of principal component analysis, which involves avoiding any initial conditional claim to achieve a certain number of factors. Both the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (KMO = .778.5) and the result of Bartlett’s test of sphericity (Chi-square = 4192; gl = 528; p < .001) showed that the statements were intercorrelated and, therefore, that factor analysis was appropriate. Varimax oblique rotation was used, yielding a chi-square ratio/degrees of freedom value of 7.73. This can be considered to indicate acceptable fit, as do the fit indices RMSEA = 0.07 and SRMR = 0.09. 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