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Article

Validation of the Academic Self-Efficacy Scale in a Latvian Adolescent Sample: A Cross-Sectional Study

by
Kristine Kampmane
* and
Antra Ozola
Faculty of Education Sciences and Psychology, University of Latvia, LV-1083 Riga, Latvia
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(8), 1082; https://doi.org/10.3390/educsci15081082
Submission received: 3 July 2025 / Revised: 15 August 2025 / Accepted: 18 August 2025 / Published: 21 August 2025
(This article belongs to the Section Education and Psychology)
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Abstract

Beliefs about one’s abilities are powerful predictors of success. Self-efficacy is a basic belief every human should have, as it reflects the confidence that one can achieve one’s goals. As this belief can change over time and depends on one’s self-reflection competence, it is defined as a skill. Academic self-efficacy extends beyond the classroom, shaping how students approach problems, set goals, and respond to challenges. There have been many attempts to create an instrument for measuring different types of self-efficacy, from general self-efficacy about life to self-efficacy to solve specific mathematical tasks. The purpose of this study was to translate, test, and adapt the Academic Self-Efficacy Scale to a sample of Latvian adolescents. The sample comprises 360 adolescents, ranging from 13-year-old sixth-grade pupils to first-year university students. The Academic Self-Efficacy Scale was validated by confirmatory factor analysis, which demonstrated excellent model fit and good item loadings. The Academic Self-Efficacy Scale demonstrated weak to moderate correlations with self-reported achievements in literature, language, and diligence. The strongest correlations were between academic self-efficacy and mathematics. Academic self-efficacy explained 23% of achievement distribution in mathematics. Achievement in mathematics together with diligence explained 32% of self-efficacy distribution. The validated scale demonstrated good reliability, convergence, and incremental validity, and the scale’s reliability and unidimensionality were approved.

1. Introduction

Beliefs about one’s abilities are powerful predictors of success. Academic self-efficacy extends beyond the classroom, shaping pupils’ abilities to become leaders (Singh et al., 2020) and work in collaboration with others (Kampmane et al., 2022; Martinsone & Vanaga, 2024) to attain more in their lives as grownups. It is believed that success produces success (Bandura, 1997), and low levels of belief in one’s self-efficacy can discourage a person from daring to try, even if one has all the necessary knowledge and skills.
In social cognitive theory, self-efficacy has been defined as a measurable confidence in one’s ability to succeed at a task (Bandura, 1977), as the skill to handle tasks without being overwhelmed (Hira, 2010), or a self-appraisal of task mastery (Pintrich et al., 1991). It is also defined as a belief in one’s competencies (Mischel, 1973), specifically cognitive capabilities rather than general feelings about oneself (Huang, 2012). It plays a crucial role in acquiring new knowledge (Branden, 2011) and influences behavior (Bandura, 2001), motivation (Pajares & Schunk, 2005), and self-regulated learning (Margolis & McCabe, 2003). These beliefs can have positive or negative effects on reasoned actions (Fishbein & Ajzen, 2011). While some consider self-efficacy the source of self-concept (Sander & Sanders, 2006), it is important not to confuse the two concepts (Huang, 2012). Stankov and Lee (2015) suggest it is based on previous experience, while Bandura (1977) identified four key sources: mastery experiences, vicarious experiences, verbal persuasion, and emotional arousal. Wesch et al. (2006) proposed a fifth source: imaginal experiences from hypothetical situations.
Many “types” of self-efficacy have been studied, from general self-efficacy (Schwarzer & Jerusalem, 1995; Sherer et al., 1982), defined as a belief in one’s competence to solve a diversity of everyday situations, to academic self-efficacy (Lent et al., 1984; Robbins et al., 2004), defined as one’s self-evaluation of success in an academic setting, and the self-efficacy of solving particular cognitive test items (OECD, 2017). However, some authors state that it would not be correct to refer to a variety of “types” of self-efficacy but rather to different measurements for different behaviors in different domains or situations that would reflect one’s sense of competence (Maddux & Volkmann, 2010). Nevertheless, there have been many debates about general self-efficacy and its predictive validity—or even whether there exist such phenomena as general self-efficacy (Woodruff & Cashman, 1993)—and whether it could be used to predict one’s well-being (Lazic et al., 2021), as the author of this concept considered self-efficacy to be task-specific (Bandura, 1997).
The same debate exists regarding students’ self-efficacy and their achievement and attainment. It is believed that self-efficacy instruments cannot demonstrate a close relationship with achievement if they are general (Bouih et al., 2021; Marsh et al., 2019), although some researchers state that general self-efficacy instruments have been proven to be as suitable as domain-specific self-efficacy measurements (Greene et al., 2004) and general academic self-efficacy measurements (Honicke & Broadbent, 2016; Nielsen et al., 2018; Robbins et al., 2004). Some authors emphasize self-efficacy’s situation-dependent variability (Bandura, 1995; Lisa, 2020), suggesting that an individual’s self-efficacy can fluctuate depending on the specific context. In contrast, others state that self-efficacy is influenced by the measurement instrument (Alabbasi et al., 2023; Marsh et al., 2019). Nevertheless, Pajares and Miller (1994) made an argument that a well-designed self-efficacy instrument can yield highly accurate predictions of outcomes.
Robbins et al. (2004) observed that academic self-efficacy was the strongest predictor of college students’ grade point average. Goetze and Driver (2022) found in their meta-analysis that academic self-efficacy explains 22% of the variability in individual academic outcomes.
There has been an ongoing debate about self-efficacy and gender: in a meta-analysis study conducted in Türkiye, Solpuk Turhan (2020) found that the difference between genders exists. Huang (2013) also concluded that the difference exists and that men have higher self-efficacy than women (although only very slightly). However, Bouih et al. (2021) found that the difference is small and favors women, while Cefai et al. (2022) found gender differences in teachers’ self-efficacy while Kampmane et al. (2022) found no gender difference in teachers’ self-efficacy. Schunk and Pajares (2002) argue that gender differences exist between domains and subsequentially in self-efficacy, although it might not be present in achievement. Nielsen et al. (2018) have drawn attention to the fact that gender must be taken into account in the analysis of academic self-efficacy, because in their research, differential item functioning was found based on gender and academic discipline.
Schunk and Pajares (2002) concluded that the greatest level of peer influence on children is between 12- and 16-year-olds and that this age might be the time when peer pressure influences pupils’ self-efficacy. However, Marsh et al. (2019) argued that self-efficacy as a concept should be peer-pressure-independent. In a longitudinal study, Vecchio et al. (2007) approved Schunk and Pajares’ (2002) findings that self-efficacy decreases in adolescents, but Zimmerman (2000) concluded that self-efficacy does not depend on a student’s age or grade.
The General Self-Efficacy measurement instrument’s unidimensionality and universality have been proven in large multinational samples (Scholz et al., 2002; Schwarzer & Born, 1997; Schwarzer & Jerusalem, 1995), while other researchers (Marsh et al., 2019; Zhou, 2016) have suggested it to be two-dimensional. Academic self-efficacy scales have so far been proven to be unidimensional (Nielsen et al., 2018; van Zyl et al., 2022).
Self-efficacy has proven a significant predictor of students’ academic and non-academic success. While there are no known instruments for measuring academic self-efficacy for the adolescent population in the Latvian language, authors have identified the following academic self-efficacy instruments published in the Scopus database (see Table 1):
As Dullas (2018) argued, academic self-efficacy scales are usually part of other scales as subscales, and finding an appropriate one might not result in success. From identified scales, none were suitable for adaptation and validation into the Latvian adolescent sample—(Greco et al., 2022; Sagone & De Caroli, 2014; and Dullas, 2018) had too many items and mostly were very specific to particular subjects, whereas (Yakimova et al., 2023; Galleguillos Herrera & Olmedo Moreno, 2017) were not in the English language. As the General Self-Efficacy Scale (Schwarzer & Jerusalem, 1995) was already adapted into the Latvian language (Griškēviča & Iltners, 2022), the authors found an academic self-efficacy scale that was constructed using its guidelines. The Academic Self-Efficacy questionnaire was first developed and validated by Lindstrom and Sharma (2011) as a measurement instrument of physics students’ physics self-efficacy but was later adapted and validated by Nielsen et al. (2018) and longitudinally validated by van Zyl et al. (2022) as a short General Academic Self-Efficacy Scale (GASE). As this scale consisted of only a few items and was adaptable to a school setting, the authors chose to adapt and validate this scale.
The main purpose of this study was to validate the GASE questionnaire in the Latvian adolescent sample. The first research question of this study was: does modified GASE questionnaire confirms psychometric properties of the original GASE (Nielsen et al., 2018; van Zyl et al., 2022)? The second research question was: how much of the variance in students’ self-reported achievement distribution can be explained by general academic self-efficacy (ASE) and vice versa, and does a student’s self-reported diligence play a role in forming ASE?
Based on the existing literature, this study expects that the adapted GASE will prove to be psychometrically sound and will confirm original GASE psychometric properties. The authors expect that ASE will account for at least 20% of the variance in students’ self-reported academic achievement, and conversely, academic achievement will account for at least 10% of academic self-efficacy, suggesting a reciprocal relationship between these constructs.

2. Materials and Methods

The sample consisted of 360 adolescents, divided into three sub-samples. The description of the sub-samples is summarized in Table 2.
The authors followed the recommendations of Guillemin et al. (1993) for the adaptation process of the questionnaire by including three stages: approval of translation from two translators and one expert, pre-testing, and final data collection. All missing data were excluded listwise. In subgroup analysis, for example, regarding gender analysis, cases with missing key data were excluded.
To assess GASE suitability for younger adolescents, prior to the main data collection, a field test was conducted on eight 6th-grade pupils. Field test revealed that two original items from the General Academic Self-Efficacy Scale (Nielsen et al., 2018), i.e., “I know I can stick to my aims and accomplish my goals in my field of study” and “I know I can pass the exam if I put in enough work during the semester,” were unclear to pupils. Consequently, these items were removed. Three new items were developed using the 10-item General Self-Efficacy Scale (GSE) (Schwarzer & Jerusalem, 1995) and added to maintain consistency with the short General Academic Self-Efficacy Scale (Nielsen et al., 2018; van Zyl et al., 2022):
  • “If I encounter a problem I’ve never seen before, I can think of at least a few ways to start solving it.” Derived from item “If I am in trouble, I can usually think of a solution.” (Schwarzer & Jerusalem, 1995)
  • “I am confident that I can do most school tasks well.” Derived from item “I am confident that I could deal efficiently with unexpected events.” (Schwarzer & Jerusalem, 1995)
  • “If I encounter difficulties in my learning tasks, I can usually solve them.” Derived from item “When I am confronted with a problem, I can usually find several solutions.” (Schwarzer & Jerusalem, 1995)
All GASE items and items’ translations into Latvian language are summarized in Appendix B (see Table A6). Answers were given in a 4-point Likert-type format, ranging from “Strongly disagree” (coded as “1”) to “Strongly agree” (coded as “4”).
Additionally to GASE items, information about pupils’/students’ age (1—13 years; 2—14 to 15 years; 3—16 to 17 years old; 4—18 to 29 years old; 5—30 years old and above), gender (1—female; 2—male; 3—left blank or “prefer not to say”), and grade/study year (1—primary school pupils (6th and 9th grade); 2—secondary school pupils (10th to 12th grade); 3—first-year university students) was collected. To complement explanatory power of the GASE, pupils/students were asked to evaluate their diligence in the current study year, “How would you evaluate your overall diligence in study work this year?” (from 1—“Not at all” to 6—“Very much”), as diligence demonstrated strong positive correlation with personality trait conscientiousness, perseverance, and academic achievement in previous study (Kampmane, 2024). Students were also asked to evaluate their academic achievement: “Mostly, what are your marks/grades in your mother tongue?”, “Mostly, what are your marks/grades in reading (literature)?”, “Mostly, what are your marks/grades in mathematics?” All self-reported achievement was coded as follows: 1—marks from 0 to 3; 2—marks from 4 to 5; 3—marks from 6 to 8; 4—marks from 9 to 10.
The data were collected and securely stored in the QuestionPro survey system. There were no conflicts of interest that could have influenced the research outcomes. Cultural norms and values were respected throughout the research process, ensuring that this study was sensitive to the diverse backgrounds of the participants. Participants were invited to this study by their teachers. Participation in a study was voluntary. Only students whose parents did not forbid participation filled in the questionnaire.
The authors of this research analyzed the data using JASP software and validated GASE using the following methods (Hughes, 2018):
  • Confirmatory factor analysis (CFA) was used in accordance with previous studies (Nielsen et al., 2018; van Zyl et al., 2022) to confirm the construct’s validity (the content and structure of the instrument). If CFA confirms previously developed factor structure with loadings above 0.4, the instrument is valid (Aithal & Aithal, 2020).
  • The average variance extracted (AVE) index was used to confirm convergent validity (Fornell & Larcker, 1981).
  • Correlation analysis and linear regression models were used to confirm concurrent and incremental validity (Hughes, 2018) as well as to explain the distribution.
To measure sample adequacy for confirmatory factor analysis (CFA), the Kaiser–Meyer–Olkin (KMO) test (Kaiser, 1974) and Bartlett’s test were used (Bartlett, 1954). A KMO value exceeding 0.7 and a significant Bartlett’s test result indicated sample adequacy (Ferguson & Cox, 1993). As factor estimation can bias CFA results (Flora & Curran, 2004), the Diagonally Weighted Least Squares (DWLS) method (Li, 2016; Mindrila, 2010) was chosen. This method is appropriate for short (4- to 5-point) Likert-type scales as it does not assume normality. Four model fit indices were used, with thresholds based on Clark and Bowles (2018):
  • Root Mean Square Error of Approximation (RMSEA): ≤0.08;
  • Standardized Root Mean Square Residual (SRMR): ≤0.10;
  • Comparative Fit Index (CFI): ≥0.90;
  • Tucker–Lewis Index (TLI): ≥0.95.
These criteria were used to determine the acceptability of the model fit to the data.

3. Results

The descriptive statistics of all GASE items are summarized in Appendix A, Table A5. To ensure newly added item consistency with the GASE items, correlation analysis was performed (see Table A1). All correlations were statistically significant at p < 0.001 ranging from 0.43 (between items “I am confident that I can do most school tasks well” and “If I encounter difficulties in my learning tasks, I can usually solve them”) to 0.72 (between items “I generally manage to solve difficult academic problems if I try hard enough” and “The motto ‘If other people can, I can too’ applies to me when it comes to my field of study”). Despite Nielsen et al. (2018) reporting that item “The motto ‘If other people can, I can too’ applies to me when it comes to my field of study” demonstrated low correlation with other items, in this study, the item’s correlation was decent. Overall item “I am confident that I can do most school tasks well” had the lowest correlation with other items. As all items correlated above 0.40, correlations were statistically significant, and as item-total correlations were 0.70 or above, the authors of this research did not remove any.
To answer the first research question, the factor structure found in previous studies (Nielsen et al., 2018; van Zyl et al., 2022) was examined (see Figure 1).
Figure 1 displays the unidimensionality of the modified GASE as it was found in previous studies (Nielsen et al., 2018; van Zyl et al., 2022) with factor loadings higher than 0.4 (Aithal & Aithal, 2020). Model fit statistics are summarized in Table 3.
According to the model fit indices, data from all samples were represented very well. The highest average variance extracted (AVE) was in the students’ sample (77%), but as AVE values for entire sample and all sub-samples exceeded the recommended threshold of 0.5, the model demonstrated adequate convergent validity. The unidimensional reliability coefficient (Cronbach’s alpha) exceeded 0.8 for entire sample, demonstrating good reliability. Factor loadings for each sub-sample and entire sample are summarized in Appendix A (see Table A2). Modified GASE psychometric properties demonstrated better model fit statistics than van Zyl et al. (2022) reported for unidimensional model at “Time 1”. As the modified scale demonstrated adequate internal consistency, and construct and convergent validity were confirmed, the answer to the first research question is that the modified GASE confirms psychometric properties of the original GASE.
To answer the second research question, each respondent’s responses on six modified GASE items were calculated by summing the total score (the score further in text is referenced as ASE). Thus, score values ranged from 6 to 24. If a respondent did not answer to all modified GASE items, he/she was excluded from further analysis.
Subsequently, a correlation analysis was performed between pupils’/students’ ASE and other items from the questionnaire of this study. Detailed correlations can be found in Appendix A (see Table A3 and Table A4), and a heatmap of correlations can be viewed in Figure 2.
Correlation between ASE and gender was not statistically significant in any of this study’s sub-samples (see Table A3) nor in the entire sample (see Figure 2, Table A4). Correlations between ASE and age were significant and positive in the entire sample (see Figure 2, Table A4) and all sub-samples except the primary school sub-sample (−0.33), where it was statistically significantly negatively correlated, i.e., pupils from ninth grade had significantly lower ASE than pupils from sixth grade (see Table A3), as was stated in previous studies (Vecchio et al., 2007). Although Zimmerman (2000) found that self-efficacy has an equal effect on achievement despite age, the results of this study show that correlation is stronger in the primary school pupil sub-sample. With the highest correlations between ASE and AMT (0.42 for entire sample and from 0.32 to 0.68 for sub-samples, see Table A3). The correlations for university students were not significant. Students did not report achievement more frequently than pupils, which might indicate that they did not remember their secondary school marks correctly, or that it did not seem relevant to answer items about their upper secondary school achievement. Significant correlations were found between pupils’/students’ ASE and their self-reported diligence in the current study year (AQ1)—for university students and primary school pupils, the correlations were 0.32 and 0.34, respectively, but for secondary school pupils, it was 0.52, indicating a moderate to strong relationship between students’ diligence and academic self-efficacy (see Table A3). Positive correlation with achievement and diligence confirm ASE’s concurrent and incremental validity.
Three linear regression equations were constructed and the explained variance by independent variables in each academic achievement domain (language, literature, mathematics) was calculated. All three linear regressions had two models—primary model (M0), where gender and grade were independent variables, and secondary model (M1), where an additional independent variable, ASE, was added. In all models, achievement in a domain was the dependent variable (see Table 4).
All equations demonstrated positive relationships. In all three linear regression models, gender and grade alone (M0) explained from 10% (AMT) to 13% (ALA) of variation in achievement. ASE explained an additional 13% of the variation in AMT (M1) while only 6% of the ALA and ALI (M1) variation. If ASE is increased by one standard deviation while gender and grade is held constant, achievement in mathematics would increase by 0.36 whereas in language by 0.26 and 0.24 in literature.
One linear regression equation was built to explain the variance in ASE. This linear regression had three models—in the primary model (M0), gender and grade were used as independent variables; in the secondary model (M1), ASE, ALI, and ALA were added as additional independent variables; but in the third model (M2), AQ1 was added as an additional independent variable. In all models, ASE was the dependent variable (see Table 5).
Gender and grade alone explained only 7% of the variance in ASE (M0). By adding achievement variables to the linear regression equation, an additional 15% were explained (M1). After adding AQ1 to the linear regression equation all independent variables, i.e., gender, grade, AMT, ALI, ALA, and AQ1 explained 32% of ASE variance (M2). The results demonstrate ASE’s relationship with diligence. If a student’s self-reported diligence is increased by one standard deviation, ASE increases by 0.36. This standardized beta value is equal to AMT’s increase by increasing ASE.
With the results from linear regression equations, the modified GASE’s incremental validity is established.

4. Discussion and Conclusions

The main purpose of this study was to validate the GASE in a Latvian adolescent sample. Field test results demonstrated the need to modify original GASE items by dropping two items and creating three new items. Similar with previous findings (Nielsen et al., 2018; van Zyl et al., 2022), the modified GASE was unidimensional and demonstrated a good fit with Latvian adolescent sample data. CFA fit statistics demonstrated good modified GASE construct and convergent validity; correlation and linear regression analysis provided modified GASE’s concurrent and incremental validity. Thus, the first research question was answered—the modified GASE confirms psychometric properties of the original GASE (Nielsen et al., 2018; van Zyl et al., 2022).
Goetze and Driver (2022) found that ASE explains 22% of academic achievement’s variance, whereas in this study, the explained variance of achievement by ASE together with gender and grade was domain-specific, ranging from 16% in literature to 22% in mathematics. Differences in correlations between achievement domains and ASE according to Nielsen et al. (2018) might indicate that general academic self-efficacy is not as general as it is proposed to be. A stronger relationship with achievement in mathematics might demonstrate that to Latvian adolescents, achievement in mathematics associates more with academic achievement than, for example, literature. Other studies (Jungert et al., 2014; Schöber et al., 2018) suggest that mathematics achievement might be more strongly related to self-efficacy, as previous achievement in mathematics influences future self-efficacy in mathematics and achievement in reading influences self-efficacy in mathematics. Although Zimmerman (2000) argued that self-efficacy has the same impact on academic achievement regardless of age, this study demonstrated that it tended to decrease with students’ grade level—the highest correlation between achievement and academic self-efficacy was seen in primary school pupils, followed by secondary school pupils, and it was not significant for university students. The latter finding might be the bias of students’ high non-response rate to questionnaire items on achievement. At the same time, academic achievement in all domains together with gender and grade explained 22% of ASE variation while only achievement in mathematics was statistically significant. These results support findings from other studies where self-efficacy’s mediating and mutual effect on academic achievement was discussed (Caprara et al., 2011; OECD, 2017). Thus, the second research question was answered. In addition, this study revealed that ASE’s explained variance was subject domain-specific and age-specific.
Previous studies where diligence was analyzed as a defining construct of personality trait conscientiousness (Costa & McCrae, 2008) supported the findings of this study that students’ self-reported diligence is strongly correlated with self-efficacy (Caprara et al., 2011; Tsai et al., 2024). In addition, this study found that correlation between self-reported diligence and ASE was stronger in the secondary school pupil sub-sample than in any other sub-samples. Another finding demonstrated ASE’s mediating role between achievement and diligence, as was found in previous studies (Caprara et al., 2011; Supervia et al., 2022).
This study confirmed previous findings (Schunk & Pajares, 2002; Vecchio et al., 2007) that ASE was lower in the 15- to 16-year-old sub-sample than in any other age group, resulting in negative correlation between ASE and age in the primary school sub-sample. As the main source of self-efficacy is a person’s previous experience (Bandura, 1977; Stankov & Lee, 2015), the strongest correlation was between age in the student sub-sample and ASE.
Other findings were in line with previous studies—academic achievement correlates between different subjects (Marsh et al., 2019), boys had weak but statistically significantly lower achievement in language-related subjects than girls (Kampmane et al., 2023), and the correlation between ASE and gender is not statistically significant (Kampmane et al., 2022; Schunk & Pajares, 2002), although some studies have found a small but significant difference (Bouih et al., 2021; Huang, 2013).
The main limitation of this study is sample size, as it is too small for the results to be generalizable. Further studies are needed to continue modified GASE validation into Latvian adolescent samples. Also, further research is needed to analyze ASE’s effects on student achievement and diligence, as well as to provide modified GASE predictive validity over time.

Author Contributions

Conceptualization, K.K.; methodology, K.K., A.O.; software, K.K.; validation, K.K., A.O.; formal analysis, K.K.; investigation, K.K.; resources, K.K.; data curation, K.K.; writing—original draft preparation, K.K.; writing—review and editing, A.O.; visualization, K.K.; supervision, A.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. Publishing costs were covered by University of Latvia, Faculty of Education Sciences and Psychology.

Institutional Review Board Statement

The research proposal was approved by the Humanities and Social Sciences Committee on Research Ethics of the University of Latvia. Acknowledgement number: 71-43/123.

Informed Consent Statement

Participation in a study was voluntary. Questionnaire was anonymous and it was not possible to identify any participant from the answers. Parental consent to allow their child participation in a study was collected on the basis of opt-out principles by sending an e-mail through school’s education management system. Stundents consent was collected on the basis of opt-in principles. By choosing to fill the questionnaire the participants expressed one’s consent to processing their answers.

Data Availability Statement

The datasets presented in this article are not readily available because of privacy restrictions. Requests to access the datasets should be directed to corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
ASEGeneal academic self-efficacy
GASEShort General Academic Self-Efficacy Scale
GSEGeneral Self-Efficacy Scale
AMTAcademic achievement in mathematics
ALAAcademic achievement in language
ALIAcademic achievement in literature
AQ1Additional question “How would you evaluate your overall diligence in study work this year?”
SE1GASE original item: “I generally manage to solve difficult academic problems if I try hard enough.”
SE2GASE original item: “I will remain calm in my exam because I know I will have the knowledge to solve the problems.”
SE3GASE original item: “The motto ‘If other people can, I can too’ applies to me when it comes to my field of study.”
SE4Newly added item: “If I encounter a problem I’ve never seen before, I can think of at least a few ways to start solving it.”
SE5Newly added item: “I am confident that I can do most school tasks well.”
SE6Newly added item: “If I encounter difficulties in my learning tasks, I can usually solve them.”
AVE Average variance extracted
CACronbach’s Alfa
RMSEARoot Mean Square Error of Approximation
SRMRStandardized root mean square residual
CFIComparative fit index
TLITucker–Lewis’s index
KMOKaiser–Meyer–Olkin test
ωMcDonald’s Omega
CIConfidence interval
CFAConfirmatory Factor Analysis
DWLSDiagonally Weighted Least Squares

Appendix A

Table A1. Pearson’s correlations of the modified GASE.
Table A1. Pearson’s correlations of the modified GASE.
VariableSE1SE2SE3SE4SE5SE6
SE20.65 **
SE30.72 **0.71 **
SE40.58 **0.54 **0.57 **
SE50.49 **0.49 **0.47 **0.47 **
SE60.53 **0.55 **0.56 **0.43 **0.43 **
SUM0.83 **0.83 **0.85 **0.76 **0.70 **0.75 **
Note. ** Significant correlations (p < 0.001). Number of cases analyzed (354 ≤ N ≤ 360). SUM—refers to summed score from all SE items, summed by each respondent.
Table A2. Factor loadings of ASE questionnaire items in CFA one-factor model of Latvian adolescent sample.
Table A2. Factor loadings of ASE questionnaire items in CFA one-factor model of Latvian adolescent sample.
FactorFactor ItemLoading Estimate95% Confidence Interval
[Lower, Upper]
PS pupilsSE10.82 *[0.76, 0.88]
SE20.85 *[0.78, 0.92]
SE30.85 *[0.79, 0.91]
SE40.70 *[0.64, 0.77]
SE50.73 *[0.66, 0.80]
SE60.70 *[0.63, 0.77]
SS pupilsSE10.88 *[0.81, 0.94]
SE20.85 *[0.78, 0.92]
SE30.91 *[0.84, 0.98]
SE40.70 *[0.63, 0.78]
SE50.56 *[0.48, 0.64]
SE60.74 *[0.67, 0.82]
StudentsSE10.90 *[0.84, 0.96]
SE20.94 *[0.88, 0.99]
SE30.97 *[0.92, 1.03]
SE40.84 *[0.77, 0.91]
SE50.88 *[0.81, 0.95]
SE60.70 *[0.62, 0.78]
Entire sampleSE10.87 *[0.83, 0.91]
SE20.87 *[0.83, 0.90]
SE30.90 *[0.87, 0.94]
SE40.74 *[0.70, 0.78]
SE50.66 *[0.62, 0.71]
SE60.72 *[0.68, 0.76]
Note. * factor loading statistically significant.
Table A3. Correlations between ASE and pupils’/students’ self-reported diligence and achievement in each domain.
Table A3. Correlations between ASE and pupils’/students’ self-reported diligence and achievement in each domain.
VariableMeasure Entire SampleStudentsSS PupilsPS Pupils
AQ1Pearson’s r0.38 **0.32 *0.52 **0.34 **
Achievement in languagePearson’s r0.29 **0.190.25 *0.40 **
Achievement in literaturePearson’s r0.24 **−0.080.26 *0.42 **
Achievement in mathematicsPearson’s r0.42 **0.240.32 **0.68 **
AgePearson’s r0.11 *0.33 *0.19 *−0.33 **
GenderPoint-Biserial r0.110.130.190.17
Note. * Significant correlations (p < 0.05 and p > 0.001); ** significant correlations (p < 0.001). Number of cases analyzed: Entire sample (gender, n = 312; age, n = 338; achievement, 335 ≤ N ≤ 337; AQ1, n = 337), students (gender, N = 65; age, N = 66; achievement, 65 ≤ N ≤ 67; AQ1, N = 66), SS pupils (gender, N = 125; age, N = 130; achievement, 130 ≤ N ≤ 131; AQ1, N = 131), PS pupils (gender, N = 118; age, N = 138; achievement, N = 136; AQ1, N = 136).
Table A4. Correlations between ASE, pupils’/students’ self-reported diligence (AQ1), achievement in each domain subject, age, grade, and gender in entire sample.
Table A4. Correlations between ASE, pupils’/students’ self-reported diligence (AQ1), achievement in each domain subject, age, grade, and gender in entire sample.
Variable ASEAQ1ALAALIAMT
AQ1Pearson’s r0.38 **—
ALAPearson’s r0.29 **0.31 **—
ALIPearson’s r0.24 **0.27 **0.48 **—
AMTPearson’s r0.42 **0.23 **0.43 **0.36 **—
AgePearson’s r0.11 *−0.100.10−0.09−0.11 *
GradePearson’s r0.15−0.14 *0.12 *0.02−0.02
GenderPoint-Biserial r0.11−0.10−0.22 *−0.30 **0.11
Note. * significant correlations (p < 0.05 and p > 0.001); ** significant correlations (p < 0.001). Number of cases: gender—308 ≤ N ≤ 312; grade—334 ≤ N ≤ 354; age—333 ≤ N ≤ 338; achievement—332 ≤ N ≤ 337; AQ1—N = 337.
Table A5. Descriptive statistics.
Table A5. Descriptive statistics.
SE1SE2SE3SE4SE5SE6
Valid360360360358359355
Missing000215
Mean2.8253.0172.8782.5112.4682.789
Std. Deviation0.9410.9500.9911.0471.0771.091
Skewness−0.229−0.543−0.461−0.0080.117−0.308
Std. Error of Skewness0.1290.1290.1290.1290.1290.129
Kurtosis−0.965−0.776−0.853−1.184−1.250−1.246
Std. Error of Kurtosis0.2560.2560.2560.2570.2570.258

Appendix B

Table A6. Items in modified GASE for Latvian adolescent sample.
Table A6. Items in modified GASE for Latvian adolescent sample.
Item’s IDItem in EnglishItem in Latvian
SE1I generally manage to solve difficult academic problems if I try hard enough.Lielākoties spēju atrisināt visas problēmas, kas man rodas mācībās.
SE2I will remain calm in my exam because I know I will have the knowledge to solve the problems.Eksāmenos un pārbaudes darbos esmu mierīgs, jo zinu, ka man būs visas nepieciešāmās zināšanas, lai atrisinātu uzdevumus.
SE3The motto ‘If other people can, I can too’ applies to me when it comes to my field of study.Apgalvojums “ja citi to var, tad es arī varu” attiecas uz mani, ja runājam par mācībām.
SE4If I encounter a problem I’ve never seen before, I can think of at least a few ways to start solving it.Ja sastopos ar uzdevumu, kuru nekad agrāk neesmu redzējis, es varu izdomāt vismaz dažus veidus, kā sākt to risināt.
SE5I am confident that I can do most school tasks well.Esmu pārliecināts, ka varu labi izpildīt lielāko daļu skolas uzdevumu.
SE6If I encounter difficulties in my learning tasks, I can usually solve them.Ja mācību darbā saskaros ar grūtībām, es parasti varu tās atrisināt.

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Education 15 01082 g001
Figure 1. CFA model of modified GASE with standardized loadings for the entire sample.
Figure 1. CFA model of modified GASE with standardized loadings for the entire sample.
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Education 15 01082 g002
Figure 2. Heatmap of correlation coefficients between ASE and other questionnaire variables. Note. AQ1—self-reported diligence in the current study year; ALA—self-reported achievement in language; ALI—self-reported achievement in Literature; AMT—self-reported achievement in mathematics; age—13 years or older; gender—1: female; 2: male; ASE—sum of modified GASE items’ values.
Figure 2. Heatmap of correlation coefficients between ASE and other questionnaire variables. Note. AQ1—self-reported diligence in the current study year; ALA—self-reported achievement in language; ALI—self-reported achievement in Literature; AMT—self-reported achievement in mathematics; age—13 years or older; gender—1: female; 2: male; ASE—sum of modified GASE items’ values.
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Table 1. Scopus-published studies where an academic self-efficacy instrument was developed or validated.
Table 1. Scopus-published studies where an academic self-efficacy instrument was developed or validated.
Main FocusNr. of ItemsSampleLanguageAuthors
University students24 items831 students from 24 Italian universitiesEnglish(Greco et al., 2022)
Undergraduate scientific students9 items170 students from Frech UniversitiesFrench(Yakimova et al., 2023)
University students30 items267 Italian university studentsEnglish(Sagone & De Caroli, 2014)
Primary and secondary school students18 items802 students in ChileSpanish(Galleguillos Herrera & Olmedo Moreno, 2017)
Primary and secondary school students50 items with 4 subscales3909 students from PhilippinesEnglish(Dullas, 2018)
Table 2. Sample description.
Table 2. Sample description.
Sub-SampleNumber of ParticipantsAgeGender
Primary School
Pupils		N = 147M = 14.42, SD = 1.19
Age range: 13–16		40% male
(14% did not indicate)
Secondary School
Pupils		N = 140M = 15.9, SD = 1.51
Age range: 15–19		35% male
(14% did not indicate)
First-Year University
Students		N = 73M = 22.90, SD = 6.77
Age range:
18–20 (N = 42)
21–28 (N = 17)
>29 (N = 8)
Missing (N = 6)		23% male
(10% did not indicate)
Table 3. One-factor model fit statistics for modified GASE from CFA for entire sample and for sub-samples.
Table 3. One-factor model fit statistics for modified GASE from CFA for entire sample and for sub-samples.
SampleRMSEASRMRCFITLIAVEωCAKMO
PS pupils0.00–0.10 (0.03)0.041.001.000.610.870.83–0.90 (0.87)0.86
SS pupils0.00–0.03 (0.00)0.031.001.000.610.870.82–0.89 (0.86)0.88
Students0.00–0.00 (0.00)0.011.001.000.770.930.88–0.94 (0.92)0.91
Entire sample0.00–0.04 (0.00)0.021.001.000.640.880.86–0.90 (0.88)0.91
Note. PS—primary school pupils’ sub-sample; SS—secondary school pupils’ sub-sample. CA—Cronbach’s Alfa; ω—McDonald’s Omega. RMSEA values are provided within 90% CI lower–upper bound interval, CA values are provided within 95% CI lower–upper bound interval. CA values are standardized and generated using parametric bootstrapping. The interval is bootstrapped.
Table 4. Linear regression models of three linear regression equations representing how student achievement was affected by age, gender, and academic self-efficacy.
Table 4. Linear regression models of three linear regression equations representing how student achievement was affected by age, gender, and academic self-efficacy.
Dependent VariableModel NumberRR2Adjusted R2Standardized Beta
AMTM00.320.100.09
M10.480.230.22ASE = 0.36 *
ALAM00.360.130.11
M10.440.190.17ASE = 0.26 *
ALIM00.350.120.11
M10.420.180.16ASE = 0.24 *
Note: ALA—self-reported achievement in language; ALI—self-reported achievement in literature; AMT—self-reported achievement in mathematics. R—multiple correlation coefficient, R2—coefficient of determination; adjusted R2—modified R2 that accounts for the number of predictors, standardized beta—standardized regression coefficient. * standardized regression coefficient statistically significant. Standardized regression coefficients were calculated for continuous predictors only.
Table 5. Linear regression models of linear regression equations representing how students’ academic self-efficacy was affected by age, gender, achievement, and diligence.
Table 5. Linear regression models of linear regression equations representing how students’ academic self-efficacy was affected by age, gender, achievement, and diligence.
Dependent VariableModel NumberRR2Adjusted R2Standardized Beta
ASEM00.240.070.06
M10.470.220.20AMT = 0.31 *
ALA = 0.10
ALI = 0.09
M20.570.320.30AMT = 0.26 *
AQ1 = 0.36 *
ALA = 0.02
ALI = 0.06
Note. AQ1—self-reported diligence in the current study year; ALA—self-reported achievement in language; ALI—self-reported achievement in literature; AMT—self-reported achievement in mathematics. R—multiple correlation coefficient; R2—coefficient of determination; adjusted R2—modified R2 that accounts for the number of predictors; standardized beta—standardized regression coefficient. * standardized regression coefficient statistically significant. Standardized regression coefficients were calculated for continuous predictors only.
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Kampmane, K.; Ozola, A. Validation of the Academic Self-Efficacy Scale in a Latvian Adolescent Sample: A Cross-Sectional Study. Educ. Sci. 2025, 15, 1082. https://doi.org/10.3390/educsci15081082

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Kampmane K, Ozola A. Validation of the Academic Self-Efficacy Scale in a Latvian Adolescent Sample: A Cross-Sectional Study. Education Sciences. 2025; 15(8):1082. https://doi.org/10.3390/educsci15081082

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Kampmane, Kristine, and Antra Ozola. 2025. "Validation of the Academic Self-Efficacy Scale in a Latvian Adolescent Sample: A Cross-Sectional Study" Education Sciences 15, no. 8: 1082. https://doi.org/10.3390/educsci15081082

APA Style

Kampmane, K., & Ozola, A. (2025). Validation of the Academic Self-Efficacy Scale in a Latvian Adolescent Sample: A Cross-Sectional Study. Education Sciences, 15(8), 1082. https://doi.org/10.3390/educsci15081082

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