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Article

Analyzing Mental Health Difficulties in Adults: New Evidence About the Strengths and Difficulties Questionnaire

by
Gloria Tomás-Gallego
,
Esther Gargallo-Ibort
,
Josep María Dalmau-Torres
and
Javier Ortuño-Sierra
*
Department of Educational Sciences, University of La Rioja, 26002 Logroño, Spain
*
Author to whom correspondence should be addressed.
Psychiatry Int. 2026, 7(2), 66; https://doi.org/10.3390/psychiatryint7020066
Submission received: 20 September 2025 / Revised: 23 January 2026 / Accepted: 26 February 2026 / Published: 17 March 2026
Versions Notes

Abstract

Background: The Strengths and Difficulties Questionnaire (SDQ) has largely been used in adolescents to screen for mental health difficulties and prosocial capabilities. The objective is to analyze the psychometric properties of the Spanish version of the SDQ in university students. Methods: This work included a large sample (N = 1256), 64.6% women with a mean age of 22.96 years (SD = 6.15). Results: The confirmatory factor analysis indicated that a six-factor solution was the most tenable. The study of measurement invariance (MI) revealed strong measurement invariance both by gender and age. The study of relations with other variables indicated that the SDQ subscales were statistically significantly correlated with indicators of self-esteem, emotional well-being, stress, and emotional regulation. Finally, the internal consistency of the scores for the Total Difficulties Score was 0.763. Conclusions: These results allow confirming the psychometric properties of the Spanish version of the SDQ for its use in university students.

1. Introduction

The World Health Organization (WHO) [1] defines Mental Health as “a state of mental well-being that enables people to cope with the stresses of life, realize their abilities, and contribute to their community”. In this regard, multiple investigations have shown that mental and emotional health problems are recurrent in youth populations [2,3,4]. In addition, prevalence rates have increased in recent years, even more after the COVID-19 pandemic [5,6,7,8]. Specifically, the group between 18 and 30 years of age, considered “emerging adults” and “young adults” have revealed particularly high rates of mental health problems [9,10,11,12].
In addition, scientific evidence has shown that university students are at a higher risk of suicidal thoughts and behaviors than the general population of the same age [13,14,15,16]. At this moment, students have to deal with new stressors, including knowing new people, living in another town, separating from their parents, and new educational challenges, which can contribute to developing psychological problems [4,10,17,18]. For instance, the study of Tomas-Gallego et al. [19] indicated that 27.80% of university students showed emotional and behavioral disorders, of which 11.20% were severe. Moreover, although they have similar levels of mental health as their non-university peers, students have greater severity of symptoms and do not return to pre-university health levels [3,20,21,22]. Considering the relevance of this moment of life for a vast number of people, instruments with adequate psychometric properties for studying psychological problems at this moment are required.
The Strengths and Difficulties Questionnaire (SDQ) is a worldwide measuring instrument that allows screening for behavioral, social, and emotional disorders, but at the same time, assesses social strengths in children and adolescents. It is made up of 25 questions answered in a Likert format scale with three options. The questions are grouped into five subscales: Emotional Problems, Conduct Problems, Peer Problems, Hyperactivity, and Prosocial Behaviors, so that strengths and weaknesses are equally well represented [23].
In addition, the SDQ fulfills the requirement to be a basic questionnaire, easy to apply, and freely available. The instrument has been translated into multiple languages, which makes it easier for cross-cultural comparisons. Worth noting, the SDQ was designed for children and adolescents from 4 to 16 years of age. Although there is also a version for adults over 18 years of age, there is hardly any literature studying this version [24,25,26].
The psychometric properties of the SDQ have been tested and validated in different studies, but, as mentioned above, most have been done in child and adolescent populations [27,28,29,30]. With regard to the evidence about the internal structure of the SDQ, results are still contradictory. Thus, different studies using confirmatory factor analysis (CFA) indicated that the five-factor solution was the most satisfactory [29,30,31,32,33,34]. However, other studies indicated that the three-factor solution composed of Internalizing symptoms (Emotional and Peer Problems subscales), Externalizing symptoms (Conduct Problems and Hyperactivity subscales), and the Prosocial subscale [35,36,37] was most satisfactory. For example, the study of the SDQ parent version, in a Spanish national sample, indicated a three-factor solution as the most satisfactory [38]. Moreover, some studies have found that a five-factor solution with two second-order factors (internalizing and externalizing) was more adequate [39]. A recent study of Skarphedinsson et al. [40] revealed that both the five-factor structure and a three-factor structure with higher-order internalizing and externalizing factors were adequate. Nonetheless, some other studies have pointed out that the inclusion of reverse-worded items is affecting the validity of the questionnaire and proposed a three-factor model comprising internalizing and externalizing and prosocial dimensions, without the mentioned items as the most adequate [41]. Considering that both the three-factor and five-factor structures have received criticism, recent work has questioned the inclusion of reverse-worded items, indicating that the internal structure of the questionnaire is adequate when these items are removed [42,43]. Worth noting, there are not, to the best of our knowledge, other studies about the psychometric properties of the SDQ in university students or young adults.
Interestingly, different studies have found contradictory results about the internal structure of the five-factor solution, indicating that the five-factor structure needed substantial modifications [29,35,44]. Moreover, a bifactor structure of the test has also been proposed [45] as a possible structure that could fit the SDQ dimensions.
As mentioned before, the evidence about the internal structure of the SDQ is still contradictory. The internal consistency of the scores has been questioned in different studies using Cronbach’s alpha. It has been argued that one of the problems explaining this could be the fact that the SDQ contains up to five positive items in the difficulties subscales, which can lead to low levels in Cronbach’s alpha coefficient and to the inconsistency of factorial solutions [46]. Furthermore, items of the SDQ are rather ordinal than continuous. Therefore, using the ordinal alpha or Omega coefficient, instead of Cronbach’s Alpha, could improve reliability indices [27,29,30,47,48].
With regard to the study of measurement invariance (MI), the existing literature is very limited, and again, there is some controversy, since it depends on the versions used and the sample. For instance, structural equivalence was found according to gender and age in the parents’ version [49]. In addition, the research by Ortuño et al. [29] showed MI by age and gender in the self- reported version of adolescents. Furthermore, Van de Looij-Jansen et al. [50] revealed MI across ethnicity and education level. The study of MI is a key aspect to be further studied because if MI does not hold, inferences could be erroneous or unfounded.
Considering that the Spanish version of the SDQ (adult form) has not yet been validated, this study aims to study the psychometric properties of the self-reported version of the SDQ for adults (+18) in Spanish. The specific objectives are to gather evidence about the internal structure of the instrument, to analyze the MI across gender and age, to study the evidence about the internal consistency of the scores, and to provide evidence in relation to other variables. We hypothesized that the five-factor solution would be the most adequate and that MI across gender and age would be found. In addition, the SDQ difficulty dimensions are expected to be positively related to measures of mental health problems.

2. Materials and Methods

2.1. Participants

The University of Northern Spain had 4400 students enrolled in the 2020–2021 academic year, distributed among the three faculties. For the selection of participants, we used a convenience sampling method. The initial sample for this study comprised 2200 students. We selected participants from different faculties and different academic years. The exclusion criteria included students who (a) were studying at a distance, (b) did not understand Spanish (e.g., exchange students), and (c) were older than 50, as they were residual and the age dispersion was amplified. Thus, the final sample comprised 1256 students. If a student did not answer a question, he or she was not allowed to continue to the next question. Thus, no incomplete responses were found.
This final sample was composed of 64.9% women (815) with a mean age of 22.96 (SD = 6.15) and an age range between 17 and 49. For this study, two groups were formed according to age, following the WHO [9,51]: (a) group 1, considered as adolescents, n = 370, age range between 17 and 19 years old; and (b) group 2, considered as “adults”, n = 886, age range between 20 and 49 years old.

2.2. Instruments

The Strengths and Difficulties Questionnaire (SDQ) allows screening for emotional and conduct problems [23]. It allows detecting behavioral, social, hyperactivity, and emotional disorders, but at the same time, it evaluates social strengths in children and adolescents. It is made up of 25 questions with responses in a Likert format with three options. The questions are grouped into five subscales with five items each: Emotional Problems, Behavioral Problems, Peer Problems, Hyperactivity, and Prosocial Behavior. In addition, there is a supplement that assesses the impact of symptoms on family life, leisure time, learning, and the relationship with friends. The SDQ is translated into many languages, and there are several versions depending on age. In the present study, the SDQ self-reported forms for adults (+18), in the Spanish version, have been used. The version is provided on the official web page of the instrument (sdqinfo.org).
The Trait Meta-Mood Scale (TMMS) is a self-report measure of emotional intelligence. The scale has been developed for the assessment of three cognitive components of the Emotional Intelligence construct, with a total of 48 items: attention to feelings, clarity, and repair [52]. In this study, the Spanish version of the 24-item scale developed by Fernandez-Berrocal’s team [53] was utilized. The items on this scale have a 5-point Likert format (1—strongly disagree, 5—strongly agree) and are balanced in positive and negative. The results must be calculated by differentiating the three components individually, and the gender of the subject.
The Rosenberg Self-Esteem Scale (RSES) is a psychometric tool designed to assess the general perception of self-esteem and worth. It is a unidimensional instrument with 10 items; 5 positively worded items (e.g., I feel that I have a number of good qualities) and 5 negatively worded items (e.g., I certainly feel useless at times), respectively, rated on a 4-point Likert-type scale (i.e., from 1 = totally disagree to 4 = totally agree) [54]. The results oscillate between 10 and 40 points, with low self-esteem being below 25 points, and high self-esteem being 30 and above. Echeburúa’s Spanish version was used for this study [55].
The Satisfaction With Life Scale (SWLS) [56] measures global life satisfaction through 5 items (e.g., I am satisfied with my life) with a 5-point Likert-type response scale, from strongly disagree (1) to strongly agree (5). The higher the total score obtained, the higher the individual’s life satisfaction. The Spanish version used for this study is from the Atienza research team [57]
The Perceived Stress Scale (PSS) was designed to measure “the degree to which individuals appraise situations in their lives as stressful” [58]. It is an instrument with 14 items (e.g., felt nervous and stressed) rated on a 5-point Likert-type scale (i.e., from 0 = never to 4 = very often). These questions concern sentiments and thoughts during the last month. Subjects are invited to indicate how often they felt a certain way. We used the Spanish adaptation of the instrument [59].

2.3. Procedure

The questionnaire was created online on the platform SurveyMonkey, and the students received the online questionnaire through the institutional email of the University. Individual responses could be submitted via any internet-connected device (i.e., mobile, tablet, or computer), from anywhere and only once. The response period was established between November 2020 and March 2021.
The survey instrument and the study were approved by the University Ethics Committee. Prior to the initiation of the survey and engagement in the academic study, participants were obliged to provide written consent following the comprehensive review of all pertinent information. Throughout the research process, the confidentiality and anonymity of the participants’ data were strictly maintained.

2.4. Data Analysis

Initially, we calculated descriptive statistics, as well as McDonald’s Omega and Ordinal alpha as measures of the internal consistency of the SDQ scores.
Second, with the aim of studying the internal structure of the SDQ, we performed different CFAs. Due to the categorical nature of the data, the Weighted Least Squares Means and Variance adjusted (WLSMV) estimator and the polychoric correlation matrix were selected. The hypothetical models that we studied were: (a) the three-factor model with Internalizing and Externalizing problems and Prosocial capabilities as dimensions; (b) the same three factor-model with the inclusion of the Correlated Errors (CE); (c) the five-factor original model [23]; (d) five factor model with CE; (e) the bifactor model that includes a general factor and five dimensions [45]; and (f) a three-factor and a five-factor models without the reverse-worded items. In order to assure the adequacy of the models studied, we used the following goodness-of-fit indices: Chi-square (χ2), Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Weighted Root Mean Square Residual (WRMR). Following Marsh, Hau, and Wen [60], RMSEA scores of about 0.05 or less are preferred; nonetheless, values below 0.08 can also be considered acceptable. In addition, CFI and TLI values over 0.95 are preferred, although values close to 0.90 are considered acceptable. Finally, WRMR values less than 0.08 indicate a good model fit [61].
Third, we performed successive multigroup CFA with the aim of studying MI [62]. The study of MI allows for confirming that the construct measured has the same structure and meaning across the groups compared [63]. In order to study MI, a configural invariance model was established with items constrained to load on the same factors across groups and allowing all item thresholds and factor loadings to vary across groups. Then, we established a strong invariance model. Attending to the well-known limitations of the ∆χ2, due to its sensitivity to sample size, Cheung and Rensvold [64] proposed the ∆CFI as a more reliable criterion for determining the equivalence of nested models. Thus, we considered a ∆CFI greater than 0.01 to reject the nested model. If the change in CFI was less than or equal to 0.01, we assumed that all specified equal constraints were tenable and we continued with the next step.
Fourth, in order to obtain evidence of validity with other variables, we studied the correlation between the SDQ dimensions and other measures of psychological well-being and psychological difficulties.
Fifth, we calculated the potential influence of gender and age in the manifestation of emotional and behavioral problems. To this end, we performed a MANOVA with the SDQ dimensions as dependent variables and gender and age as independent variables.
SPSS 24.0 [65] and JASP 0.18.3 version [66] were used for data analyses.

2.5. Patient and Public Involvement

Students were involved in this research in two phases. In the first phase, through the study design. A preliminary phase was conducted with a pilot sample of 25 university students prior to the collection of data. These students were not involved in the final study sample. The objective of this phase was to evaluate the clarity of the questions, the comprehension of the items, the time taken, and the functionality of the online survey. After the preliminary phase, minor adjustments were made to the wording of some items to enhance comprehension, particularly in the sections on alcohol consumption and physical activity. For example, in questions related to alcohol consumption, the response option “0” was included. The same approach was applied to questions on physical activity, and an example of the type of physical activity described to enhance clarity was also included. In the second phase, they participated by answering the questionnaire that was created ad hoc.

3. Results

3.1. Evidence of Validity Based on Internal Structure

As shown in Table 1, the goodness-of-fit indices for the three and five-factor baselines were inadequate. For both models, we found substantial Modification Indices (MIs) (i.e., ≥25) for error correlation between items 2 (restless) and 10 (fidgeting). As can be seen, both items belong to the Hyperactive subscale, and the meaning is similar, suggesting the possibility that this subscale could have overlapping items. Also, other CEs suggest the possibility of overlapping between items from different subscales. In order to consider the inherent problem in the use of correlated errors [67], and from a pragmatic standpoint, it was decided that a single correlated error should be computed in the analyses out of 180 potential CE. Therefore, the model is still far from being fully saturated.
The inclusion of these CE generated an increase in the goodness-of-fit indices of all the models tested. Nonetheless, the three-factor model was still close to inadequate, with TLI close to 0.90. Therefore, we studied a six-factor solution where the hyperactive factor was divided into impulsive and inattentional symptoms. This solution displayed better fit indices than the previous model, and no error correlations were needed. Hence, the decision was taken to retain this model as the most satisfactory solution.
Factor loadings of the five-factor model without reverse-worded items and item 15 were calculated. As shown in Table 2, all factor loadings met the criterion of statistical significance, ranging from 0.30 (23: I get on better with adults than with people my own age) to 0.993 (3: I finish the work I’m doing. My attention is good). Factor correlations are shown to be statistically significant, with a range from −0.31 (Emotional Problems and Prosocial) to 0.84 (Conduct Problems and Hyperactivity).

3.2. Measurement Invariance of the SDQ Scores Across Gender and Age

Once the five-factor model without reverse-worded items revealed the best goodness-of-fit indices, we tested the measurement equivalence of this factor structure across gender and age (see Table 3). To examine MI across age, the sample was divided into two different subgroups: adolescents (n = 370) and adults (n = 886). Then, we examined configural, strong, and metric MI. Differences in ΔCFI below 0.01 between the models supported the hypothesis of strong MI across both gender and age.

3.3. Validity Evidence Based on the Relation with Other Variables

Pearson’s correlations between the PSS, Rosenberg, SWLS, TMMS, and SDQ subscales and total score were calculated with the aim of gathering evidence of the relationship between the proposed SDQ model and other indicators of mental health. As shown in Table 4, most of the correlations between SDQ dimensions and the rest of the instruments were statistically significant. Nonetheless, the attention subscale of the TMMS was not significantly correlated with Conduct Problems, Inattention, Impulsivity Problems, and Peer Problems, and the repair subscale was not correlated with Conduct Problems. In addition, the PSS total score was not correlated with Prosocial abilities.

3.4. Descriptive Statistics, Mean Comparisons, and Inter Internal Consistency of the Scores for the Five-Dimensional Model Without Reverse-Worded Items

The descriptive statistics of the five subscales and the total score of the SDQ (i.e., mean and standard deviation) are displayed in Table 5 for the whole sample, and considering gender (i.e., male and female) and age (i.e., emerging adulthood and adults). The MANOVA indicated statistically significant differences by gender. No significant differences were found by age. Thus, we studied the differences by gender. The ANOVA indicated statistically significant differences in all of the subscales but Hyperactivity and the total score. Men scored higher in Conduct Problems, whereas women displayed higher scores in Internalizing Problems, Peer Problems, and Prosocial Behavior.
In addition, the reliability of the scores was analyzed using McDonald’s omega and ordinal alpha (see Table 4). The McDonald’s ω for the total score was 0.713, and for the subscales it ranged from 0.465 (Peer Problems) to 0.789 (Emotional Problems).

4. Discussion

The principal aim of the present study was to analyze the psychometric properties of the SDQ in its Spanish form for adults. Although the SDQ has been largely used and its psychometric properties have been analyzed in adolescent samples, the present study is, to the best of our knowledge, the first study to examine the psychometric adequacy of its Spanish version for adults.
First, we studied the internal structure of the questionnaire. Results found in the present study are somewhat similar to other studies [29,30,33,68,69], revealing that the original five-factor structure better fits the data when compared to the three-factor solution. Nonetheless, and similarly to previous studies [29,35,44], the five-factor structure without any modification did not fit the data properly. We then studied the proposed [42,43] five and three-factor solutions without the reverse-worded items. The results showed that both the three and five-factor models increased model fit, but still needed correlated errors to be added. It should be noted that other results in Spanish populations indicated that a three-factor solution was satisfactory [38], although in this case, it was the SDQ parents’ version. Despite the multitude of studies analyzing the psychometric properties of the SDQ in children and adolescents, only a few studies have analyzed the psychometric properties of the SDQ in adults [24], which precludes comparison between our results and previous studies. The present work found a six-dimensional model as the most satisfactory, with the separation of the hyperactivity dimension into inattention and impulsive symptoms. It might be that for young adults, these two aspects are separated compared to adolescents or children, who understand this as part of the same construct. Moreover, the factor loadings were all significant for the proposed six-factor model.
We then studied the MI of the five-factor model without the reverse-worded items across gender and age. As previous studies found [29,30,34,44], MI was found both by gender and age. It is worth noting that some other studies indicated that only partial MI was found by gender [31,50]. Again, the lack of studies about the adults’ version precludes the comparison of our results with regard to the study of MI with previous studies. The study of MI is crucial when it comes to assuring the comparison between measures of psychological problems and prosocial capabilities, attending to relevant variables like gender or age.
We also studied the evidence of the relation with other indicators of mental health. The results indicated that the subscales of the SDQ were positively and statistically significantly correlated with measures of stress and negatively correlated with measures of self-esteem, well-being, and emotional intelligence. Worth noting, no correlation was found between measures of stress and prosocial behavior, contrary to other studies that indicate a negative statistically significant correlation between stress and prosocial behavior [70]. Other studies have also revealed that the SDQ was correlated with measures of psychological difficulties like problematic internet use [71] and suicide behavior [72].
Finally, we analyzed the descriptive statistics and the internal consistency of the scores. The prevalence rates for the difficulties subscales are similar to those found in previous studies analyzing mental health problems. Moreover, we studied the potential influence of gender and age on the expression of emotional and behavioral difficulties. The results showed that women scored higher than men in emotional difficulties and peer problems, whereas men scored higher for conduct problems. These results are similar to previous research with adolescents [73], although no results are available with the SDQ in adult populations. We did not find differences by age. In addition, the study of the internal consistency of the scores revealed adequate levels for all subscales except Peer Problems (0.465). Also, the Hyperactivity and Prosocial subscales showed indices below 0.60, which are still poor. These results limit the score interpretability and suggest that the subscales, especially Peer Problems, should be reformulated in terms of item composition. The total score revealed good reliability of the scores with an Omega value of 0.713. Previous literature has some contradictory results with good scores for some subscales and low scores for others [36,74]. Studies using ordinal Alpha found better indices of reliability but were inconsistent in some cases [29,30,31,50,75]. One of the strengths of the SDQ is the fact that it is an instrument composed of subscales with a reduced number of items. This may explain the low indices of internal consistency of the scores. Thus, new research is still needed in order to confirm the reliability of the scores in the adult version of the SDQ.
The present work has some limitations that should be noted. The information gathered by questionnaires has inherent problems and biases. For this reason, future studies could try to implement other research techniques such as experimental data or brain image techniques (e.g., neuroimage, electrogram, etc.). Also, the study was based on a specific region of the Spanish territory and with university students, which may preclude the generalization of the results to other regions of the country and to other populations. Moreover, the convenience sampling limits the external validity of the study and could have caused selection bias. Therefore, future studies should analyze the psychometric properties in representative samples of young adults. The present study includes different measures of psychological well-being and mental health; nonetheless, we did not include specific measures of psychopathology (e.g., anxiety or depression scales), which limits the evidence of validity with external variables. In addition, the study was cross-sectional in nature, and no test–retest information was analyzed, which limits the possibility of establishing cause-and-effect relationships and obtaining more evidence about the reliability of the study. Also, the study is limited to a particular region of the north of the country and specifically in one University, which limits the generalization of the results. Worth noting, we conducted a previous pilot study to ensure that the items were appropriate for the sample. Thus, future longitudinal studies could add valuable information in this regard. Besides the noted limitations, the present work has clear implications for the aim to use the SDQ in young adults. To date, and to the best of our knowledge, this is one of the first and largest samples studying the psychometric adequacy of the adult version of the SDQ. The proposed six-factor solution could help researchers to study mental health problems and prosocial behaviors in university students, and not limit the use of the SDQ to the screening of mental health problems in adolescent populations.

Author Contributions

Methodology, G.T.-G. and J.O.-S.; Investigation, E.G.-I., J.M.D.-T. and J.O.-S.; Resources, J.M.D.-T.; Writing—original draft, G.T.-G. and J.O.-S.; Supervision, G.T.-G. and E.G.-I. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Government of La Rioja. Department of Education and Employment. Research Plan for the year 2026. grant number REGI2025/10.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the University of La Rioja Ethics Committee (protocol code rVGMmMvkfVdA05wUtVEifww6IDkItSiy and date of approval 12 March 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical/privacy issues.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. WHO. Promoción de La Salud Mental. Conceptos, Evidencia Emergente y Práctica; Informe Compendiado; WHO: Ginebra, Switzerland, 2004. [Google Scholar]
  2. Meadows, S.O.; Brown, J.S.; Elder, G.H. Depressive symptoms, stress, and support: Gendered trajectories from adolescence to young adulthood. J. Youth Adolesc. 2006, 35, 99–103. [Google Scholar] [CrossRef]
  3. Ramón-Arbués, E.; Gea-Caballero, V.; Granada-López, J.M.; Juárez-Vela, R.; Pellicer-García, B.; Antón-Solanas, I. The prevalence of depression, anxiety and stress and their associated factors in college students. Int. J. Environ. Res. Public Health 2020, 17, 7001. [Google Scholar] [CrossRef]
  4. Hernández-Torrano, D.; Ibrayeva, L.; Sparks, J.; Lim, N.; Clementi, A.; Almukhambetova, A.; Nurtayev, Y.; Muratkyzy, A. Mental health and well-being of university students: A bibliometric mapping of the literature. Front. Psychol. 2020, 11, 1226. [Google Scholar] [CrossRef]
  5. Ocaña-Moral, M.T.; Gavín-Chocano, Ó.; Pérez-Navío, E.; Martínez-Serrano, M.d.C. Relationship among perceived stress, life satisfaction and academic performance of education sciences students of the University of Jaén after the COVID-19 pandemic. Educ. Sci. 2021, 11, 802. [Google Scholar] [CrossRef]
  6. Rogowska, A.M.; Ochnik, D.; Kuśnierz, C.; Jakubiak, M.; Schütz, A.; Held, M.J.; Arzenšek, A.; Benatov, J.; Berger, R.; Korchagina, E.V.; et al. Satisfaction with life among university students from nine countries: Cross-National study during the first wave of COVID-19 pandemic. BMC Public Health 2021, 21, 2262. [Google Scholar] [CrossRef]
  7. Ruiz-Zaldibar, C.; García-Garcés, L.; Vicario-Merino, Á.; Mayoral-Gonzalo, N.; Lluesma-Vidal, M.; Ruiz-López, M.; Pérez-Manchón, D. The impact of COVID-19 on the lifestyles of university students: A Spanish online survey. Healthcare 2022, 10, 309. [Google Scholar] [CrossRef]
  8. Zhang, S.X.; Chen, R.Z.; Xu, W.; Yin, A.; Dong, R.K.; Chen, B.Z.; Delios, A.Y.; Miller, S.; McIntyre, R.S.; Ye, W.; et al. A systematic review and meta-analysis of symptoms of anxiety, depression, and insomnia in Spain in the COVID-19 crisis. Int. J. Environ. Res. Public Health 2022, 19, 1018. [Google Scholar] [CrossRef]
  9. Arnett, J.J.; Žukauskiene, R.; Sugimura, K. The new life stage of emerging adulthood at ages 18–29 years: Implications for mental health. Lancet Psychiatry 2014, 1, 569–576. [Google Scholar] [CrossRef]
  10. Cleary, M.; Walter, G.; Jackson, D. “Not always smooth sailing”: Mental health issues associated with the transition from high school to college. Issues Ment. Health Nurs. 2011, 32, 250–254. [Google Scholar] [CrossRef]
  11. Zacarés González, J.J.; Garcia, O.F. Convertirse en adulto hoy: La «transición interminable». In Nuevas Miradas en Psicología del ciclo Vital; Sandoval-Obando, E., Serra Defilis, E., García, Ó.F., Eds.; Ril Editores: Santiago, Chile, 2020; pp. 121–152. [Google Scholar]
  12. Becht, A.I.; Nelemans, S.A.; Branje, S.J.T.; Vollebergh, W.A.M.; Meeus, W.H.J. Daily Identity Dynamics in Adolescence Shaping Identity in Emerging Adulthood: An 11-Year Longitudinal Study on Continuity in Development. J. Youth Adolesc. 2021, 50, 1616–1633. [Google Scholar] [CrossRef] [PubMed]
  13. Mortier, P.; Demyttenaere, K.; Auerbach, R.P.; Cuijpers, P.; Green, J.G.; Kickens, G.; Kessler, R.C.; Nock, M.K.; Zaslavsky, A.M.; Bruffaerts, R. First Onset of Suicidal Thoughts and Behaviours in College. J. Affect. Disord. 2017, 207, 291–299. [Google Scholar] [CrossRef]
  14. Mortier, P.; Cuijpers, P.; Kiekens, G.; Auerbach, R.P.; Demyttenaere, K.; Green, J.G.; Kessler, R.C.; Nock, M.K.; Bruffaerts, R. The Prevalence of Suicidal Thoughts and Behaviours among College Students: A Meta-Analysis. Psychol. Med. 2018, 48, 554–565. [Google Scholar] [CrossRef]
  15. Ward, C.; McLafferty, M.; McLaughlin, J.; McHugh, R.; McBride, L.; Brady, J.; Bjourson, A.J.; Walsh, C.P.; O’Neill, S.M.; Murray, E.K. Suicidal Behaviours and Mental Health Disorders among Students Commencing College. Psychiatry Res. 2022, 307, 114314. [Google Scholar] [CrossRef]
  16. Eskin, M.; Sun, J.M.; Abuidhail, J.; Yoshimasu, K.; Kujan, O.; Janghorbani, M.; Flood, C.; Carta, M.G.; Tran, U.S.; Mechri, A.; et al. Suicidal Behavior and Psychological Distress in University Students: A 12-Nation Study. Arch. Suicide Res. 2016, 20, 369–388. [Google Scholar] [CrossRef] [PubMed]
  17. Akram, U.; Ypsilanti, A.; Gardani, M.; Irvine, K.; Allen, S.; Akram, A.; Drabble, J.; Bickle, E.; Kaye, L.; Lipinski, D.; et al. Prevalence and Psychiatric Correlates of Suicidal Ideation in UK University Students. J. Affect. Disord. 2020, 272, 191–197. [Google Scholar] [CrossRef]
  18. Gall, T.L.; Evans, D.R.; Bellerose, S.; Gall, T.L. Transition to First-Year University: Patterns of Change in Adjustment across Life Domains and Time. J. Soc. Clin. Psychol. 2000, 19, 544–567. [Google Scholar] [CrossRef]
  19. Tomás-Gallego, G.; Jiménez Boraita, R.; Ortuño Sierra, J.; Gargallo Ibort, E.; Dalmau Torres, J.M. Emotional and Behavioural Problems in Spanish University Students: Association with Lifestyle Habits and Mental Well-Being. Healthcare 2024, 12, 1482. [Google Scholar] [CrossRef] [PubMed]
  20. Auerbach, R.P.; Mortier, P.; Bruffaerts, R.; Alonso, J.; Benjet, C.; Cuijpers, P.; Demyttenaere, K.; Ebert, D.D.; Green, J.G.; Hasking, P.; et al. WHO World Mental Health Surveys International College Student Project: Prevalence and Distribution of Mental Disorders. J. Abnorm. Psychol. 2018, 127, 623–638. [Google Scholar] [CrossRef] [PubMed]
  21. Bewick, B.; Koutsopouloub, G.; Miles, J.; Slaad, E.; Barkham, M. Changes in Undergraduate Students’ Psychological Well-Being as They Progress through University. Stud. High. Educ. 2010, 35, 633–645. [Google Scholar] [CrossRef]
  22. Hunt, J.; Eisenberg, D. Mental Health Problems and Help-Seeking Behavior Among College Students. J. Adolesc. Health 2010, 46, 3–10. [Google Scholar] [CrossRef]
  23. Goodman, R. The Strengths and Difficulties Questionnaire: A Research Note. J. Child Psychol. Psychiatry 1997, 38, 581–586. [Google Scholar] [CrossRef] [PubMed]
  24. Brann, P.; Lethbridge, M.J.; Mildred, H. The Young Adult Strengths and Difficulties Questionnaire (SDQ) in Routine Clinical Practice. Psychiatry Res. 2018, 264, 340–345. [Google Scholar] [CrossRef]
  25. Glenn, S.; Cunningham, C.; Nananidou, A.; Prasher, V.; Glenholmes, P. Using the Strengths and Difficulties Questionnaire with Adults with Down Syndrome. Res. Dev. Disabil. 2013, 34, 3343–3351. [Google Scholar] [CrossRef]
  26. Riglin, L.; Agha, S.S.; Eyre, O.; Bevan Jones, R.; Wootton, R.E.; Thapar, A.K.; Collishaw, S.; Stergiakouli, E.; Langley, K.; Thapar, A. Investigating the Validity of the Strengths and Difficulties Questionnaire to Assess ADHD in Young Adulthood. Psychiatry Res. 2021, 301, 113984. [Google Scholar] [CrossRef] [PubMed]
  27. Español-Martín, G.; Pagerols, M.; Prat, R.; Rivas, C.; Sixto, L.; Valero, S.; Artigas, M.S.; Ribasés, M.; Ramos-Quiroga, J.A.; Casas, M.; et al. Strengths and Difficulties Questionnaire: Psychometric Properties and Normative Data for Spanish 5- to 17-Year-Olds. Assessment 2021, 28, 1445–1458. [Google Scholar] [CrossRef] [PubMed]
  28. García, P.; Goodmann, R.; Mazaira, J.; Torres, A.; Rodríguez-Sacristán, J.; Hervas, A.; Fuentes, J. El Cuestionario de Capacidades y Dificultades. Rev. Psiquiatr. Infanto-Juv. 2000, 1, 12–17. [Google Scholar]
  29. Ortuño-Sierra, J.; Chocarro, E.; Fonseca-Pedrero, E.; Riba, S.S.I.; Muñiz, J. The Assessment of Emotional and Behavioural Problems: Internal Structure of The Strengths and Difficulties Questionnaire. Int. J. Clin. Health Psychol. 2015, 15, 265–273. [Google Scholar] [CrossRef] [PubMed]
  30. Ortuño-Sierra, J.; Fonseca-Pedrero, E.; Paino, M.; Sastre I Riba, S.; Muñiz, J. Screening Mental Health Problems during Adolescence: Psychometric Properties of the Spanish Version of the Strengths and Difficulties Questionnaire. J. Adolesc. 2015, 38, 49–56. [Google Scholar] [CrossRef]
  31. Bøe, T.; Hysing, M.; Skogen, J.C.; Breivik, K. The Strengths and Difficulties Questionnaire (SDQ): Factor Structure and Gender Equivalence in Norwegian Adolescents. PLoS ONE 2016, 11, e0152202. [Google Scholar] [CrossRef] [PubMed]
  32. De Vries, P.J.; Davids, E.L.; Mathews, C.; Aarø, L.E. Measuring Adolescent Mental Health around the Globe: Psychometric Properties of the Self-Report Strengths and Difficulties Questionnaire in South Africa, and Comparison with UK, Australian and Chinese Data. Epidemiol. Psychiatr. Sci. 2018, 27, 369–380. [Google Scholar] [CrossRef]
  33. Van Roy, B.; Veenstra, M.; Clench-Aas, J. Construct Validity of the Five-Factor Strengths and Difficulties Questionnaire (SDQ) in Pre-, Early, and Late Adolescence. J. Child Psychol. Psychiatry 2008, 49, 1304–1312. [Google Scholar] [CrossRef]
  34. He, J.P.; Burstein, M.; Schmitz, A.; Merikangas, K.R. The Strengths and Difficulties Questionnaire (SDQ): The Factor Structure and Scale Validation in U.S. Adolescents. J. Abnorm. Child Psychol. 2013, 41, 583–595. [Google Scholar] [CrossRef] [PubMed]
  35. Percy, A.; McCrystal, P.; Higgins, K. Confirmatory Factor Analysis of the Adolescent Self-Report Strengths and Difficulties Questionnaire. Eur. J. Psychol. Assess. 2008, 24, 43–48. [Google Scholar] [CrossRef]
  36. Ruchkin, V.; Jones, S.; Vermeiren, R.; Schwab-Stone, M. The Strengths and Difficulties Questionnaire: The Self-Report Version in American Urban and Suburban Youth. Psychol. Assess. 2008, 20, 175–182. [Google Scholar] [CrossRef]
  37. Koskelainen, M.; Sourander, A.; Vauras, M. Self-Reported Strengths and Difficulties in a Community Sample of Finnish Adolescents. Eur. Child Adolesc. Psychiatry 2001, 10, 180–185. [Google Scholar] [CrossRef] [PubMed]
  38. Gómez-Beneyto, M.; Nolasco, A.; Moncho, J.; Pereyra-Zamora, P.; Tamayo-Fonseca, N.; Munarriz, M.; Salazar, J.; Tabarés-Seisdedos, R.; Girón, M. Psychometric Behaviour of the Strengths and Difficulties Questionnaire (SDQ) in the Spanish National Health Survey 2006. BMC Psychiatry 2013, 13, 95. [Google Scholar] [CrossRef]
  39. Goodman, A.; Lamping, D.L.; Ploubidis, G.B. When to Use Broader Internalising and Externalising Subscales Instead of the Hypothesised Five Subscales on the Strengths and Difficulties Questionnaire (SDQ): Data from British Parents, Teachers and Children. J. Abnorm. Child Psychol. 2010, 38, 1179–1191. [Google Scholar] [CrossRef]
  40. Skarphedinsson, G.; Njardvik, U. Psychometric Properties of the Strengths and Difficulties Questionnaire in an Icelandic Community Sample. Nord. J. Psychiatry 2024, 78, 705–712. [Google Scholar] [CrossRef] [PubMed]
  41. Sourander, A.; Westerlund, M.; Kaneko, H.; Heinonen, E.; Klomek, A.B.; How Ong, S.; Fossum, S.; Kolaitis, G.; Lesinskiene, S.; Li, L.; et al. Cross-Cultural Comparison of the Strengths and Difficulties Self-Report Questionnaire in 12 Asian and European Countries. J. Am. Acad. Child Adolesc. Psychiatry 2025, 64, 799–809. [Google Scholar] [CrossRef]
  42. Kankaanpää, R.; Töttö, P.; Punamäki, R.L.; Peltonen, K. Is It Time to Revise the SDQ? The Psychometric Evaluation of the Strengths and Difficulties Questionnaire. Psychol. Assess. 2023, 35, 1069–1084. [Google Scholar] [CrossRef]
  43. Duinhof, E.L.; Duinhof, E.L.; Lek, K.M.; De Looze, M.E.; Cosma, A.; Mazur, J.; Gobina, I.; Wüstner, A.; Vollebergh, W.A.M.; Stevens, G.W.J.M. Epidemiology and Psychiatric Sciences Revising the Self-Report Strengths and Difficulties Questionnaire for Cross-Country Comparisons of Adolescent Mental Health Problems: The SDQ-R. Epidemiol. Psychiatr. Sci. 2019, 29, e35. [Google Scholar] [CrossRef]
  44. Rønning, J.A.; Handegaard, B.H.; Sourander, A.; Mørch, W.T. The Strengths and Difficulties Self-Report Questionnaire as a Screening Instrument in Norwegian Community Samples. Eur. Child Adolesc. Psychiatry 2004, 13, 73–82. [Google Scholar] [CrossRef]
  45. Kóbor, A.; Takács, Á.; Urbán, R. The Bifactor Model of the Strengths and Difficulties Questionnaire. Eur. J. Psychol. Assess. 2013, 29, 299–307. [Google Scholar] [CrossRef]
  46. Stone, L.L.; Otten, R.; Engels, R.C.M.E.; Vermulst, A.A.; Janssens, J.M.A.M. Psychometric Properties of the Parent and Teacher Versions of the Strengths and Difficulties Questionnaire for 4- to 12-Year-Olds: A Review. Clin. Child Fam. Psychol. Rev. 2010, 13, 254–274. [Google Scholar] [CrossRef] [PubMed]
  47. Goodman, R. Psychometric Properties of the Strengths and Difficulties Questionnaire. J. Am. Acad. Child Adolesc. Psychiatry 2001, 40, 1337–1345. [Google Scholar] [CrossRef]
  48. Theunissen, M.H.C.; de Wolff, M.S.; Reijneveld, S.A. The Strengths and Difficulties Questionnaire Self-Report: A Valid Instrument for the Identification of Emotional and Behavioral Problems. Acad. Pediatr. 2019, 19, 471–476. [Google Scholar] [CrossRef] [PubMed]
  49. Murray, A.L.; Speyer, L.G.; Hall, H.A.; Valdebenito, S.; Hughes, C. A Longitudinal and Gender Invariance Analysis of the Strengths and Difficulties Questionnaire Across Ages 3, 5, 7, 11, 14, and 17 in a Large U.K.-Representative Sample. Assessment 2021, 29, 1248–1261. [Google Scholar] [CrossRef]
  50. Van De Looij-Jansen, P.M.; Goedhart, A.W.; De Wilde, E.J.; Treffers, P.D.A. Confirmatory Factor Analysis and Factorial Invariance Analysis of the Adolescent Self-Report Strengths and Difficulties Questionnaire: How Important Are Method Effects and Minor Factors? Br. J. Clin. Psychol. 2011, 50, 127–144. [Google Scholar] [CrossRef]
  51. Arnett, J.J. Emerging Adulthood: A Theory of Development from the Late Teens through the Twenties. Am. Psychol. 2000, 55, 469–480. [Google Scholar] [CrossRef]
  52. Salovey, P.; Mayer, J.D.; Goldman, S.L.; Turvey, C.; Palfai, T.P. Emotional Attention, Clarity and Repair: Exploring Emotional Intelligence Using the Trait Meta-Mood Scale. In Emotion, Disclosure, and Health; Pennebaker, J.W., Ed.; American Psychological Association: Washington, DC, USA, 1995; pp. 125–154. [Google Scholar]
  53. Fernández-Berrocal, P.; Extremera, N.; Ramos, N. Validity and Reliability of the Spanish Modified Version of the Trait Meta-Mood Scale. Psychol. Rep. 2004, 94, 751–755. [Google Scholar] [CrossRef] [PubMed]
  54. Rosenberg, M. Society and the Adolescent Self-Image; Princeton University Press: Princeton, NJ, USA, 1965. [Google Scholar]
  55. Salaberria Irízar, K.; Echeburúa Odriozola, E. Tratamiento psicológico de la fobia social: Un estudio experimental. Análisis Modif. Conducta 1995, 21, 151–179. [Google Scholar]
  56. Diener, E.D.; Emmons, R.A.; Sem, R.J.L.; Griffin, S. The Satisfaction with Life Scale. J. Pers. Assess. 1985, 49, 71–75. [Google Scholar] [CrossRef]
  57. Atienza, F.L.; Pons, D.; Balaguer, I.; García-Merita, M. Propiedades Psicométricas de La Escala de Satisfacción con La Vida En Adolescentes. Psicothema 2000, 12, 314–319. [Google Scholar]
  58. Cohen, S.; Kamarck, T.; Mermelstein, R. A Global Measure of Perceived Stress. J. Health Soc. Behav. 1983, 24, 385–396. [Google Scholar] [CrossRef]
  59. Remor, E. Psychometric Properties of a European Spanish Version of the Perceived Stress Scale (PSS). J. Psychol. 2006, 9, 86–93. [Google Scholar] [CrossRef] [PubMed]
  60. Marsh, H.W.; Hau, K.T.; Wen, Z. In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler’s (1999) Findings. Struct. Equ. Model. 2004, 11, 320–341. [Google Scholar] [CrossRef]
  61. Hu, L.T.; Bentler, P.M. Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Struct. Equ. Model. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  62. Byrne, B.M. Testing for Multigroup Equivalence of a Measuring Instrument: A Walk through the Process. Psicothema 2008, 20, 872–882. [Google Scholar]
  63. Byrne, B.M.; van de Vijver, F.J.R. Testing for Measurement and Structural Equivalence in Large-Scale Cross-Cultural Studies: Addressing the Issue of Nonequivalence. Int. J. Test. 2010, 10, 107–132. [Google Scholar] [CrossRef]
  64. Cheung, G.W.; Rensvold, R.B. Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance. Struct. Equ. Model. 2002, 9, 233–255. [Google Scholar] [CrossRef]
  65. IBM SPSS Statistics for Windows, version 24.0; IBM Corporation: Armonk, NY, USA, 2016.
  66. JASP, version 0.18.3; JASP Team: Amsterdam, The Netherlands, 2024.
  67. Heene, M.; Hilbert, S.; Freudenthaler, H.H.; Bühner, M. Sensitivity of SEM Fit Indexes with Respect to Violations of Uncorrelated Errors. Struct. Equ. Model. 2012, 19, 36–50. [Google Scholar] [CrossRef]
  68. Göran Svedin, C.; Priebe, G. The Strengths and Difficulties Questionnaire as a Screening Instrument in a Community Sample of High School Seniors in Sweden. Nord. J. Psychiatry 2008, 62, 225–232. [Google Scholar] [CrossRef]
  69. Liu, S.K.; Chien, Y.L.; Shang, C.Y.; Lin, C.H.; Liu, Y.C.; Gau, S.S.F. Psychometric Properties of the Chinese Version of Strength and Difficulties Questionnaire. Compr. Psychiatry 2013, 54, 720–730. [Google Scholar] [CrossRef]
  70. Forbes, P.A.G.; Aydogan, G.; Braunstein, J.; Todorova, B.; Wagner, I.C.; Lockwood, P.L.; Apps, M.A.J.; Ruff, C.C.; Lamm, C. Acute Stress Reduces Effortful Prosocial Behaviour. eLife 2023, 12, RP87271. [Google Scholar] [CrossRef]
  71. Ortuño-Sierra, J.; Pérez-Sáenz, J.; Mason, O.; Pérez de Albéniz, A.; Fonseca-Pedrero, E.; Fonseca Pedrero, E. Problematic Internet Use among Adolescents: Spanish Validation of the Compulsive Internet Use Scale (CIUS). Adicciones 2022, 36, 247–256. [Google Scholar] [CrossRef] [PubMed]
  72. Fonseca-Pedrero, E.; Díez-Gómez, A.; de la Barrera, U.; Sebastian-Enesco, C.; Ortuño-Sierra, J.; Montoya-Castilla, I.; Lucas-Molina, B.; Inchausti, F.; Pérez-Albéniz, A. Suicidal Behaviour in Adolescents: A Network Analysis. Span. J. Psychiatry Ment. Health 2020, 17, 3–10. [Google Scholar] [CrossRef]
  73. Ortuño-Sierra, J.; Sebastián-Enesco, C.; Pérez-Albéniz, A.; Lucas-Molina, B.; Fonseca-Pedrero, E. Spanish Normative Data of the Strengths and Difficulties Questionnaire in a Community-Based Sample of Adolescents: Datos Normativos Españoles Del Cuestionario de Capacidades y Dificultades (SDQ) En Una Muestra Comunitaria de Adolescentes. Int. J. Clin. Health Psychol. 2022, 22, 100328. [Google Scholar] [CrossRef] [PubMed]
  74. Ruchkin, V.; Koposov, R.; Vermeiren, R.; Schwab-Stone, M. The Strength and Difficulties Questionnaire: Russian Validation of the Teacher Version and Comparison of Teacher and Student Reports. J. Adolesc. 2012, 35, 87–96. [Google Scholar] [CrossRef]
  75. Ezpeleta, L.; Granero, R.; De La Osa, N.; Penelo, E.; Domènech, J.M. Psychometric Properties of the Strengths and Difficulties Questionnaire in 3-Year-Old Preschoolers. Compr. Psychiatry 2013, 54, 282–291. [Google Scholar] [CrossRef]
Table 1. Goodness-of-fit indices for the different models tested.
Table 1. Goodness-of-fit indices for the different models tested.
MODELχ2DfCFITLIRMSEA (CI 90%)WRMR
Baseline one-factor model14,588.6243000.6650.6600.115 (0.111–0.1191.021
Three-factor model2533.2472720.8420.8250.081 (0.076–0.087)0.106
Three-factor model with CE1255.4562680.8000.8090.0696 (0.061–0.073)
Five-factor model1710.1062650.8320.810.066 (0.063–0.069)0.088
Five-factor model with CE (2–10)1055.8542640.9080.8950.049 (0.046–0.052)0.077
Three factors without reverse-worded items1729.4041670.8270.8010.076 (0.072–0.082)0.095
Three factors without reverse-worded items and CE (2–10)738.7921660.9190.9040.052 (0.049–0.056)0.078
Five-factor model without reverse-worded items1056.8051600.8770.8540.066 (0.062–0.070)0.089
Five factors without reverse-worded items and CE (2–10)512.3811590.9390.9230.045 (0.041–0.049)0.066
Note. CE = Correlated errors added; χ2 = Chi square; Df = degrees of freedom; CFI = Comparative Fit Index; TLI = Tucker–Lewis Index; RMSEA = Root Mean Square Error of Approximation; WRMR = Weighted Root Mean Square Residual; CI = Confidence IntervalFit Index.
Table 2. Estimated saturation item parameters for the five-factor model without reverse-worded items.
Table 2. Estimated saturation item parameters for the five-factor model without reverse-worded items.
ItemsEstimateErrorLowerUpper
Emotional
30.6150.0080.5700.637
80.8120.0090.7820.844
130.8280.0120.8070.881
160.7040.0150.6790.745
240.7200.0170.6890.764
Conduct
50.6250.0290.5790.679
120.6450.0310.5560.690
180.7010.0290.6320.769
220.4170.0280.3260.473
Peer Problems
60.5200.0230.4650.555
190.6400.0290.5940.706
230.3100.0210.2590.340
Hyperactivity
20.8100.0360.7590.899
100.8380.0350.7650.912
150.7920.0320.7550.875
Prosocial
10.6190.0300.5630.681
40.3340.0270.3020.406
90.8120.0310.7380.861
170.6400.0310.5820.697
200.5660.0290.5010.596
Table 3. Study of the measurement invariance of the Strengths and Difficulties Questionnaire across gender and age.
Table 3. Study of the measurement invariance of the Strengths and Difficulties Questionnaire across gender and age.
χ2DfCFITLIRMSEA
(CI 90%)		WRMRΔCFI
Gender
Male (n = 441)511.9851590.9340.910.043 (0.038–0.051)0.062
Female (n = 815)513.8521590.9320.930.047 (0.039–0.050)0.067
Configural Invariance520.1597750.9310.9430.043 (0.039–0.051)0.061
Strong Invariance523.6578500.9280.9410.051 (0.040–0.058)0.063−0.01
Metric Invariance519.8519010.9310.9390.050 (0.040–0.057)0.062−0.01
Age
Adolescents (n = 370)510.5821590.9400.9390.045 (0.036–0.052)0.059
Adults (n = 886)512.2331590.9380.9310.048 (0.039–0.056)0.063
Configural Invariance519.2857750.9300.9280.049 (0.040–0.058)0.065
Strong Invariance522.6588500.9290.9270.051 (0.042–0.060)0.066−0.01
Metric invariance521.1589010.9240.9220.050 (0.042–0.059)0.064−0.01
Note. χ2 = Chi square; Df = degrees of freedom; CFI = Comparative Fit Index; TLI = Tucker–Lewis Index; RMSEA = Root Mean Square Error of Approximation; WRMR = Weighted Root Mean Square Residual; CI = Confidence Interval; ΔCFI = Change in Comparative Fit Index.
Table 4. Evidence of the relation of the Strengths and Difficulties Questionnaire with other variables.
Table 4. Evidence of the relation of the Strengths and Difficulties Questionnaire with other variables.
SDQPSS TotalRosenberg TotalSWLS TotalTMMS
AttentionClarityRepair
Emotional Problems0.680 **−0.591 **−0.420 **0.297 **−0.353 **−0.405 **
Conduct Problems0.184 **−0.132 **−0.129 **−0.007−0.069 **−0.155 **
Hyperactivity0.389 **−0.379 **−0.338 **0.034−0.271 **−0.253 **
Peer Problems0.315 **−0.360 **−0.312 **0.029−0.172 **−0.223 **
Prosocial−0.0380.106 **0.109 **0.230 **0.107 **0.135 **
Total Score0.619 **−0.571 **−0.458 **0.160 **−0.345 **−0.379 **
** Note: The correlation is significant at the 0.01 level (bilateral). SDQ = Strengths and Difficulties Questionnaire; PSS = Perceive Stress Scale; SWLS = Subjective Wellbeing Life Scale; TMMS = Trait Meta-Mood Scale.
Table 5. Descriptive statistics of the five subscales and the total score of the SDQ.
Table 5. Descriptive statistics of the five subscales and the total score of the SDQ.
SDQM (SD)Reliability Indices
Gender Age Total
(N = 1256)
Male
(n = 441)		Female
(n = 815)		p (η2)Adolescents
(n = 370)		Adults
(n = 886)		p (η2) McDonald’s ω/Ordinal Alpha
(95% CI)
Internalizing Problems2.955 (2.52)4.461 (2.83)0.001 (0.043)4.122 (2.81)3.154 (2.69)0.001 (0.043)3.931 (2.82)0.789/0.792
Conduct Problems2.188 (1.38)2.019 (1.31)0.001 (0.041)2.115 (1.34)1.953 (1.07)0.03 (0.013)2.083 (1.34)0.671/0.673
Hyperactivity2.11 (0.71)2.24 (0.67)0.368 (0.002)2.24 (0.69)2.11 (0.64)0.15 (0.001)2.22 (0.74)0.510/0.523
Peer Problems2.432 (1.61)2.498 (1.61)0.02 (0.011)2.476 (1.60)2.462 (1.62)0.26 (0.002)2.473 (1.61)0.465/0.48
Prosocial7.86 (1.86)8.754 (1.33)0.01 (0.010)8.457 (1.62)8.356 (1.53)0.23 (0.003)8.437 (1.60)0.588/0.591
Total Score11.69 (5.16)12.710 (5.44)0.047 (0.003)12.691 (5.39)10.93 (5.03)0.02 (0.010)12.344 (5.37)0.763/0.773
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Tomás-Gallego, G.; Gargallo-Ibort, E.; Dalmau-Torres, J.M.; Ortuño-Sierra, J. Analyzing Mental Health Difficulties in Adults: New Evidence About the Strengths and Difficulties Questionnaire. Psychiatry Int. 2026, 7, 66. https://doi.org/10.3390/psychiatryint7020066

AMA Style

Tomás-Gallego G, Gargallo-Ibort E, Dalmau-Torres JM, Ortuño-Sierra J. Analyzing Mental Health Difficulties in Adults: New Evidence About the Strengths and Difficulties Questionnaire. Psychiatry International. 2026; 7(2):66. https://doi.org/10.3390/psychiatryint7020066

Chicago/Turabian Style

Tomás-Gallego, Gloria, Esther Gargallo-Ibort, Josep María Dalmau-Torres, and Javier Ortuño-Sierra. 2026. "Analyzing Mental Health Difficulties in Adults: New Evidence About the Strengths and Difficulties Questionnaire" Psychiatry International 7, no. 2: 66. https://doi.org/10.3390/psychiatryint7020066

APA Style

Tomás-Gallego, G., Gargallo-Ibort, E., Dalmau-Torres, J. M., & Ortuño-Sierra, J. (2026). Analyzing Mental Health Difficulties in Adults: New Evidence About the Strengths and Difficulties Questionnaire. Psychiatry International, 7(2), 66. https://doi.org/10.3390/psychiatryint7020066

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