Faculty expectations and academic achievement: a multilevel analysis of the Pygmalion Effect in university students

Paúl Andrade-Ubidia; Carla B.Gudiño-Mejía; Nelson Salazar; Evelyn Hernández-Martínez

doi:10.12688/f1000research.177579.2

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Research Article

Revised

Faculty expectations and academic achievement: a multilevel analysis of the Pygmalion Effect in university students

[version 2; peer review: 1 approved]

Paúl Andrade-Ubidia¹, Carla B.Gudiño-Mejía¹, Nelson Salazar¹, Evelyn Hernández-Martínez¹

PUBLISHED 05 May 2026

Author details Author details

¹ FECYT, Universidad Tecnica del Norte, Ibarra, Imbabura Province, Ecuador

Paúl Andrade-Ubidia
Roles: Conceptualization, Methodology, Supervision, Validation, Writing – Review & Editing

Carla B.Gudiño-Mejía
Roles: Conceptualization, Formal Analysis, Methodology, Resources, Software, Validation, Writing – Original Draft Preparation

Nelson Salazar
Roles: Data Curation, Visualization, Writing – Review & Editing

Evelyn Hernández-Martínez
Roles: Data Curation, Writing – Review & Editing

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Abstract

Background

The quality of education provided by higher education institutions is largely dependent on the interpersonal relationships established between students and faculty members. Using the principles of the Pygmalion Effect, this study examined the impact of teachers’ expectations on the academic performance of first-year students at the Technical University of the North.

Methods

An explanatory, cross-sectional research approach incorporating a quantitative methodology and multilevel modelling techniques was employed to analyse the data. A total of 476 students and 30 teachers participated in the study. These participants responded to five validated psychometric instruments (Aiken’s V > 0.85; Cronbach’s α 0.79–0.88).

Results

An exploratory factor analysis identified three factors in students that accounted for 69.23% of the variance: Classroom Climate, Negative Interactions and Perceived Expectations. In the study of teaching practices, the explained variance of 59.20% was distributed across three factors: Expectations, Theories of Intelligence and Perception of Potential. Multilevel regression analysis revealed that teacher expectations were the paramount predictor of performance (B = 1.18; p < 0.001). Specifically, the analysis showed that for each unit increase in these expectations, the academic average rose by 1.18 points, thereby surpassing the impact of student perceptions.

Conclusions

These results provide substantial evidence in support of the Pygmalion effect in a university setting. These results highlight the importance of re-examining the expectations placed on teachers during their professional training.

Keywords

pygmalion effect, teacher expectations, self-fulfilling prophecy, academic achievement, higher education, classroom climate

Corresponding authors: Paúl Andrade-Ubidia, Carla B.Gudiño-Mejía

Competing interests: No competing interests were disclosed.

Grant information: Universidad Técnica del Norte [grant number UTN-2024-FH-EDU-015]
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2026 Andrade-Ubidia P et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Andrade-Ubidia P, B.Gudiño-Mejía C, Salazar N and Hernández-Martínez E. Faculty expectations and academic achievement: a multilevel analysis of the Pygmalion Effect in university students [version 2; peer review: 1 approved]. F1000Research 2026, 15:413 (https://doi.org/10.12688/f1000research.177579.2) First published: 19 Mar 2026, 15:413 (https://doi.org/10.12688/f1000research.177579.1) Latest published: 05 May 2026, 15:413 (https://doi.org/10.12688/f1000research.177579.2)

Revised Amendments from Version 1

First, a methodological clarification was added to Section 2.3 (Exploratory Factor Analysis) to explicitly state that items with factor loadings below .40 on all retained factors were excluded from the model. This criterion is now also referenced in the Results section (Section 3.1) where the exclusion of the Response Opportunities dimension (ND1-D4) is reported.
Second, the Limitations section was expanded to include a notation regarding the number of Level 2 units (n = 30 faculty members), which is somewhat below the sample sizes typically recommended for reliable multilevel parameter estimation at the group level. Readers are advised to interpret faculty-level coefficients with appropriate caution, while noting that the overall findings remain valid given the robustness of the Level 1 student sample.
Third, the Limitations section was further expanded to frame the marginally significant effects of Negative Interactions (FE2; p = .092) and Student-Perceived Faculty Expectations (FE3; p = .055) as theoretically meaningful trends consistent with established research, rather than interpreting them solely on statistical grounds.
Fourth, a direct reference to the FigShare repository was added to the Data Availability section to facilitate access to complete item-specific information for researchers interested in replicating the study.
No changes were made to the study design, data, analyses, results, or conclusions reported

To read any peer review reports and author responses for this article, follow the "read" links in the Open Peer Review table.

1. Introduction

The study by Rubie-Davies and Hattie (2024) highlights the substantial contribution of Merton’s introduction of the theory of the self-fulfilling prophecy in 1948 to scholarly discourse. According to Merton’s theory, the beliefs of a third party can shape the reality experienced by another individual. Eden (1992) further argues that McGregor first elucidated the theoretical underpinnings of the Pygmalion effect in 1960 when he formulated a theory of organizational behavior. This was a foundational contribution to the field, as was Likert’s (1961) work, which emphasized the importance of communication in achieving high levels of performance. Bushra (2024) notes that the concept of the Pygmalion effect also emerged in the 1960s from the studies of Rosenthal and Jacobson (1968). These studies demonstrated that students identified as high performers by their teachers exhibited significantly greater improvement compared to their peers who lacked similar expectations.

Since then, numerous researchers have engaged with this area of study to determine which student and teacher characteristics lead educators to hold high or low expectations for students and how these expectations affect academic performance. Sitnikova (2023), in her systematic review, notes that historical approaches to the subject have included a range of methodologies, from positivist paradigms to more critical and qualitative approaches. Furthermore, she indicates that the concept is now accepted as a social process influenced by structural and cultural factors. The Pygmalion technique has been observed in educational settings and business environments. In the professional sphere, it has been used to enhance worker efficiency and productivity (Farrukh et al., 2025; Yilmaz & Bayram, 2023).

The Pygmalion effect, which has been examined in relation to the impact of parents’ expectations on students’ academic performance, has been the focus of research (Muñoz, 2023). In the medical field, this effect has been used to enhance students’ motivation, confidence, and self-esteem. However, its appropriate implementation is crucial to prevent students who do not receive such positive attention from feeling marginalized or undervalued (Homayouni-Zand & Kalantarion, 2023). As Bergold and Steinmayr (2023) have noted, previous studies have shown that educators tend to overestimate their students. Furthermore, these studies demonstrate that underestimation has adverse effects on students, whereas overestimation has beneficial effects.

Gentrup et al. (2020) found that teachers’ expectations are often inaccurate, which can influence student performance. Therefore, this study is based on the premise that the social environment and teachers’ beliefs are malleable and influential factors that play a key role in determining academic success. This stance contrasts with deterministic perspectives that attribute performance to genetic predispositions (Matthews et al., 2024).

When discussing the Pygmalion effect and the self-fulfilling prophecy, it is important to consider their interrelationship, as in both cases, expectations or beliefs are delegated to third parties, rendering the subject passive and leading them to conform to the imposed prophecy (Quintero et al., 2022). This phenomenon examines the impact of implicit beliefs on behavior, particularly with regard to cognitive and emotional loads. In this context, it is important to note that the quality, not the quantity, of teacher-student interaction shapes teachers’ perceptions of each student at the individual level (Nolkemper et al., 2019).

Recent studies, including those by Rubie-Davies and Hattie (2024) and Papageorge et al. (2020), have reported significant improvements when high expectations are placed on students. In this context, educational psychology must address and harness the Pygmalion effect.

Nevertheless, the phenomenon continues to raise theoretical questions about its origins. For example, one might question whether teaching professionals unconsciously form expectations about their students in the classroom. Furthermore, if such expectations are indeed present, it is imperative to identify the underlying mechanisms that give rise to them and the factors that drive them. A related question concerns whether the Pygmalion effect occurs in a conventional educational context. In such an environment, students must recognize that teachers have higher expectations for some students than for others and subsequently act accordingly.

These questions motivate the present research, which aims to examine the impact of teacher expectations on the academic performance of first-semester students at the Universidad Técnica del Norte (UTN; Technical University of the North) through the lens of the Pygmalion effect.

To achieve this objective, an evaluation was conducted using a two-level multistage model. The first level consists of teachers. Within this level, we sought to examine teachers’ expectations, their implicit theories about intelligence, and their perceptions of students’ potential. The second level consists of students, where we assessed their perceptions of their teachers’ expectations and the classroom environment.

This research is being conducted at the university’s Faculty of Education, Science, and Technology (FECYT), a key setting for professional training. This context provides valuable evidence in the region and addresses a gap in Latin American literature, given the preponderance of North American and European studies.

2. Methods

The proposed research employed a non-experimental, observational, explanatory, and applied design, utilizing a quantitative approach. Furthermore, it follows deductive reasoning, incorporating correlational and predictive analyses. The study aims to identify how faculty expectations influence the performance of first-semester students at FECYT, employing multilevel statistical analysis within the educational context.

Additionally, this study is grounded in the existing theory of the Pygmalion effect and seeks to identify the factors of the theoretical model that play a role in the context of university students and professors.

The target population consisted of first-semester students and faculty members from the Faculty of Education, Science, and Technology (FECYT). Non-probabilistic convenience sampling was employed, inviting the entire available student and teaching population of the Faculty’s first level to participate. No exclusion criteria were established, with the objective of maximizing sample representativeness. Consequently, from January 15, 2025, to March 16, 2025, responses were obtained from 476 students and 30 professors, achieving total participation from the faculty and a response rate exceeding 90% from the first-semester student body.

The study protocol was reviewed and approved by the Research Ethics Committee of the Universidad Técnica del Norte (approval number: UTN-FECYT-CEI-2025-003-C; date: 13 January 2025) and was conducted in accordance with the Declaration of Helsinki. Prior to participation, written informed consent was obtained from all subjects via electronic forms.

2.1 Instruments

For the development of this study, two levels of analysis were established: Pygmalion from the faculty level and the student level. For the faculty level, the following instruments were applied: the Teacher Expectation Questionnaire (TEQ), aimed at measuring faculty expectations; the Implicit Theories of Intelligence Scale (ITIS), focused on the fixed or malleable view of intelligence; and the Student Potential Perception Scale (SPPS), which evaluates faculty perception of student potential.

For the student level, the Student Perception of Expectations (SPED), which collects the student’s perception of the teacher’s expectations, and the Classroom Climate Scale (CCS), which measures classroom climate from the student perspective, were utilized.

All instruments were adapted and validated specifically for this research, based on previously established theories and constructs, which allowed for structuring key dimensions for the analysis of the Pygmalion effect and identifying its main components in the higher education context.

Classroom climate was assessed using an adaptation of the Classroom Climate Scale (CCS), developed and validated for the Ecuadorian context based on both Latin American and international theoretical and empirical references. The version used was grounded in three key studies: the three-dimensional model proposed by Sriklaub et al. (2015), oriented towards student-centered learning; the TCCS scale by Sørlie and Hukkelberg (2023), which evaluates climate from the faculty perception; and the work of López et al. (2018), who validated a classroom climate instrument in Chilean students using a mixed approach and mass application.

For the construction of the instrument evaluating the perception of faculty expectations (SPED) regarding the Pygmalion effect, the definition of the constructs was primarily based on the study by Witty and Debaryshe (1994), which allowed for structuring parallel items for faculty and students, grouped into dimensions clearly defined through factor analysis. Complementarily, empirical and theoretical references were incorporated from Ardiles and Escobar (1997), who addressed student expectations regarding the teaching role in personal, pedagogical, and technical dimensions; from Gürşimşek (1999), whose work evidenced the differential perception of teacher treatment towards students of different performance levels; and from López et al. (2018), who validated a classroom climate scale from a Latin American perspective.

The design of the Student Potential Perception Scale (SPPS) instrument was based on the study by Ceroni et al. (2016), who identified three key dimensions in faculty perception of the student body: academic potential, learning difficulties, and inappropriate behavior. Based on this theoretical and psychometric structure, the original items were adapted and expanded for application in the Ecuadorian higher education context. The questionnaire allowed for obtaining a reliable measure of the faculty’s anticipatory judgment regarding the student’s future performance, an essential component of the Pygmalion effect.

The present research relies on the work of Abd-El-Fattah and Yates (2006), who present a scale with 14 items on entity and incremental intelligence beliefs, grounded in the underlying theory. The ITIS is supported by an exploratory and confirmatory factor analysis, affirming the existence of two differentiated and psychometrically solid factors. These reliability coefficients indicate that the subscales of the obtained factors exceed.75, which is considered acceptable. Structural equivalence was also verified in student samples completing questionnaires at universities in Australia and Egypt, as well as between males and females, supporting the cross-cultural validity of the scale’s use. Additionally, the results presented by Liu (2021) are included, arguing the influence that these types of beliefs exert on achievement and intrinsic motivation. Through this scale, this work seeks to answer how the beliefs faculty hold regarding the nature of intelligence affect the expectations they set for their students, which constitutes the core of the Pygmalion effect.

The design of the Teacher Expectation Questionnaire (TEQ) instrument is fundamentally based on the theoretical model developed by Brophy (1983), which systematized research on educational expectations and the role they play in generating self-fulfilling prophecies within the school context. This author stated that the expectations education professionals have regarding their students’ capabilities translate into observable interaction patterns, particularly in affective climate, feedback, level of demand, and opportunities for participation, which configured the design of the TEQ. The contributions of Cooper (1979), who formulates a model on the diffusion of expectations and the impact they have on performance, were also added, as well as those of Rubie-Davies (2006), whose empirical research showed that teacher expectations cumulatively affect the academic self-evaluation students grant themselves.

It should be noted that each instrument was composed of various dimensions measured through Likert-type scale items. The organizational structure is detailed in Figure 1.

Figure 1. Structure of instruments, dimensions, and items applied by variable level (ND and NA).

Note: ND = Faculty Level (from Spanish Nivel Docente). TEQ = Teacher Expectation Questionnaire. ND1-D1 = Emotional climate. ND1-D2 = Feedback. ND1-D3 = Level of demand and instruction. ND1-D4 = Response opportunities. ITIS = Implicit Theories of Intelligence Scale. ND2-D1 = Fixed view. ND2-D2 = Malleable view of intelligence. SPPS = Student Potential Perception Scale. ND3-D1 = Potential in the classroom. ND3-D2 = Learning difficulties. ND3-D3 = Inappropriate behavior. ND3-D4 = Socioemotional skills. NA = Student Level (from Spanish Nivel Alumno). SPED = Student Perception of Expectations. NA1-D1 = Promotion of student participation and involvement. NA1-D2 = Personal consideration toward the student. NA1-D3 = Avoidance of negative interactions. NA1-D4 = Lack of recognition or praise. CCS = Classroom Climate Scale. NA2-D1 = Physical environment. NA2-D2 = Faculty-student interactions. NA2-D3 = Peer relationships. NA2-D4 = Faculty orientation toward learning. P = Number of questions per dimension.

2.2 Analysis of instrument reliability and validity

The validation and relevance of the instruments’ content were evaluated through expert judgment (n = 3), ensuring that the items adequately reflect and cover the construct measured in each category. Aiken’s V coefficient was utilized to quantify the consensus among the judges regarding the pertinence of each item. Subsequently, the Delphi method was applied to consolidate consensus regarding the final structure of the instruments.

Aiken’s V test showed high content validity: the TEQ obtained a V coefficient of .85; ITIS, .88; and SPPS, .90. Regarding the student level, the SPED and CCS obtained V coefficients of .87 and .89, respectively. This demonstrates that, in all cases, the items present a significant degree of agreement among the experts, supporting their content validity.

Internal consistency was evaluated using Cronbach’s α coefficient, yielding the following results: for the TEQ, a coefficient of .81; ITIS, .79; and SPPS, .85, regarding the student-level instruments, the SPED and CCS reached coefficients of .83 and .88, respectively. This reflects satisfactory and consistent reliability, confirming the psychometric adequacy of the instruments for application in the study.

2.3 Exploratory Factor Analysis (EFA)

Data processing and statistical analysis were performed using Jamovi 2.3 software, based on the R language, utilizing robust extraction methods.

Spearman correlation matrices were constructed to explore bivariate relationships between the dimensions of the instruments administered to faculty and students. This preliminary analysis allowed for evaluating the strength and direction of associations between variables, as well as verifying the statistical suitability of applying Exploratory Factor Analysis (EFA) by identifying significant covariation patterns among the items.

An EFA was conducted with the purpose of reducing the dimensionality of faculty and student perceptions and revealing the underlying structure within each dimension. In the case of students, eight theoretical dimensions related to classroom climate, faculty expectations, and negative interactions were grouped. For this purpose, the Minimum Residuals (MinRes) method was employed—suitable when multivariate normality is not assumed—along with an orthogonal Varimax rotation to obtain interpretable factors.

The number of factors was determined using Horn’s Parallel Analysis, which compares actual eigenvalues with those generated from random matrices, supported by Kaiser’s criterion (eigenvalues >1). This procedure indicated the retention of three factors which, after Varimax rotation, collectively explained 69.23% of the total variance:

• Student Factor 1 (FE1) – Classroom Climate
• Student Factor 2 (FE2) – Negative Interactions
• Student Factor 3 (FE3) – Faculty Expectations

In terms of proportional explained variance, FE1 accounted for 42.25%, FE2 for 29.16%, and FE3 for 28.60% of the factorial model. Factor loadings greater than 0.40 were considered significant, ensuring that each item was solidly associated with a single factor, thus guaranteeing a clear and coherent conceptual structure for subsequent use in regression models.

The EFA for the faculty scale was conducted using the same methodological strategy, applying the MinRes method and Varimax rotation. In an initial stage, all proposed dimensions were included; however, items that did not reach a factor loading of .40 on any factor were excluded to ensure parsimony and structural clarity. Specifically, the “Opportunities” dimension (ND1_D4) was excluded because its dominant loading fell below this threshold (i.e., it did not reach .40 on any retained factor).

Subsequently, three factors emerged with loadings greater than .40, labeled according to the predominant dimensions:

• Faculty Factor 1 (FD1) – Theories of Intelligence
• Faculty Factor 2 (FD2) – Faculty Expectations
• Faculty Factor 3 (FD3) – Perception of Student Potential

These three factors explained 59.20% of the total cumulative variance. Of the variance explained by the model, FD1 contributed 34.83%, FD2 contributed 35.20%, and FD3 contributed 30.08%, reflecting a balanced distribution among the identified constructs.

2.4 Multilevel regression analysis

To evaluate the predictive capacity of the factors identified in the EFA regarding academic achievement, a hierarchical modeling strategy was followed. In a first phase, a robust regression model was fitted, including exclusively the student-level factors (Level 1). In this initial stage, all student dimensions showed statistically significant effects on academic performance.

Subsequently, to incorporate the hierarchical structure of the data and comply with the multilevel approach, faculty-level factors (Level 2) were introduced, specifically those linked to expectations (FD2). The inclusion of second-level predictors generated a variation in the student-level coefficients, showing that faculty expectations act as a determinant predictor of performance.

Thus, it was possible to demonstrate that the inclusion of the second level of analysis offers a more complete perspective of educational dynamics, suggesting that the effect of student perceptions can be moderated or enhanced through faculty expectations.

The proposed hierarchical multilevel model was:

(1)

Y_{ij} = β_{0} + β_{1} X_{1 ij} + β_{2} X_{2 ij} + β_{3} X_{3 ij} + β_{4} Z_{j} + u_{0 j} + ε_{ij}

Where:

$Y_{ij}$ : academic achievement of student i in classroom j.

$β_{0}$ : global intercept (general average of achievement).

$X_{1 ij}$ : student factor 1 (FE1 – Classroom Climate).

$X_{2 ij}$ : student factor 2 (FE2 – Negative Interactions).

$X_{3 ij}$ : student Factor 3 (FE3 – Faculty Expectations).

$Z_{j}$ : faculty factor (FD2 – Faculty Expectations).

$β_{1} \dots β_{4}$ : estimated regression coefficients (fixed effects).

$u_{0 j}$ : random effect of the faculty level (variance between professors).

$ε_{ij}$ : random error of the student level (residual variance).

This mathematical model validates the multilevel analysis by decomposing the error term into two components: the variability attributed to the faculty member ( $u_{0 j}$ ) and the individual variability of the student ( $ε_{ij}$ ).

3. Results

The findings obtained from the analysis of faculty and student perceptions in relation to academic achievement are presented below, in order to identify the manifestation of the Pygmalion Effect in the university context.

3.1 Descriptive analysis and factor structure

Initially, an Exploratory Factor Analysis (EFA) was conducted with the purpose of revealing the latent structure underlying the evaluated dimensions. This procedure allowed for the identification of several distinct conceptual factors for both faculty and students based on the original dimensions.

Based on this factorial structure, composite variables were generated which were subsequently employed as predictors in the regression models. These models allow for analyzing how the perceptions collected through the Faculty Level (ND) and Student Level (NA) instruments are associated with academic performance, empirically evidencing the factors that configure the Pygmalion effect in higher education.

In Figure 2, significant positive associations are evidenced among the Emotional Climate (ND1-D1), Feedback (ND1-D2), and Level of Demand and Instruction (ND1-D3) dimensions of the TEQ instrument. Spearman correlation coefficients of $r = .55$ between Emotional Climate and Feedback, $r = .62$ between Emotional Climate and Level of Demand, and $r = .64$ between Feedback and Level of Demand are identified, all with high statistical significance (p < .001). This covariation suggests the presence of a strong shared latent component linked to explicit expectations.

Figure 2. Spearman correlation matrix, distributions, and scatterplots among faculty-level dimensions.

The main diagonal displays the distribution of each variable. The lower triangle presents the scatterplots, and the upper triangle indicates the Spearman correlation coefficients (r). Asterisks indicate statistical significance: * p < .05, ** p < .01, *** p < .001. ND = Faculty Level (from Spanish Nivel Docente).

Regarding the Malleable View of Intelligence (ND2-D2) dimension, results show a significant correlation with the Potentialities (ND3-D1) dimension ( $r = .64$ , p = .001). In contrast, the Learning Difficulties (ND3-D2) and Inappropriate Behavior (ND3-D3) dimensions present weaker correlations ( $r$ between .40 and .43) with the remaining variables, and in several cases do not reach statistical significance. This reinforces the notion that these variables represent differentiated constructs with a lower degree of structural dependence within the faculty model.

In Figure 3, significant positive correlations are observed among the student-level dimensions. Of note is a strong association between Faculty-student interactions (NA2-D2) and Peer relationships (NA2-D3), with a coefficient of $r = .72$ (p < .001), as well as between Physical environment (NA2-D1) and Faculty-student interactions ( $r = .66$ , p < .001). These results reflect a structured covariation pattern among the Classroom Climate Scale (CCS) dimensions.

Figure 3. Spearman correlation matrix, distributions, and scatterplots among student-level dimensions.

The main diagonal displays the density of the variables. The upper triangle contains the correlation coefficients (r) and the lower triangle the corresponding scatterplots. * p < .05, ** p < .01, *** p < .001. NA = Student Level (from Spanish Nivel Alumno).

Likewise, relevant correlations are identified among the SPED instrument dimensions, particularly between Promotion of student participation and involvement (NA1-D1) and Personal consideration toward the student (NA1-D2) ( $r = .68, p < 0.001$ ), as well as between Avoidance of negative interactions (NA1-D3) and Lack of recognition or praise (NA1-D4) ( $r = .71, p < .001$ ). These findings suggest a coherent student perception regarding faculty treatment and its emotional and motivational implications.

Figure 4 presents the faculty-level factor loadings, organized into three latent factors.

Figure 4. Exploratory factor model and standardized loadings of the faculty level (ND).

The diagram shows the factor loadings (λ) obtained in the EFA. FD1 = Theories of intelligence; FD2 = Faculty expectations; FD3 = Perception of potential. Curved arrows indicate correlations between latent factors. ND = Faculty Level (from Spanish Nivel Docente).

The first factor, Theories of Intelligence (FD1), integrates Fixed View (ND2-D1) (λ = 1.00) and Malleable View (ND2-D2) (.68). Both dimensions, evaluated with the Implicit Theories of Intelligence Scale, reflect faculty beliefs regarding the student’s cognitive plasticity. The high loading of the Fixed View indicates that this dimension centrally defines the construct.

The second factor, Faculty Expectations (FD2), groups the dimensions of the Teacher Expectation Questionnaire: Emotional Climate (ND1-D1) (.59), Feedback (ND1-D2) (.90), and Level of Demand and Instruction (ND1-D3) (.64). These dimensions evidence that expectations are operationalized in the creation of an affective environment, the quality of feedback, and academic rigor.

The third factor, Perception of Student Potential (FD3), derived from the Student Potential Perception Scale, includes Potential in the Classroom (ND3-D1) (.79), Learning Difficulties (ND3-D2) (.63), and Inappropriate Behavior (ND3-D3) (.76). The lowest loading was observed in Socioemotional Skills (ND3-D4) (.42). These associations indicate that faculty judgment is a multidimensional construction encompassing positive attributes and perceived deficits.

Finally, the Response Opportunities (ND1-D4) dimension was excluded from the model due to presenting an insufficient loading (below .40 on all retained factors).

Figure 5 details the factorial structure of the student level, identifying three conceptual factors that explain student perception regarding the Pygmalion effect in the university context.

Figure 5. Exploratory factor model and standardized loadings of the student level (NA).

Factor loadings (λ) are presented on the unidirectional arrows. FE1 = Classroom Climate; FE2 = Negative Interactions; FE3 = Faculty Expectations. NA = Student Level (from Spanish Nivel Alumno).

The first factor, Classroom Climate (FE1), groups the dimensions of the Classroom Climate Scale. The Faculty-student interactions dimension (.96) acts as the principal component, followed by Peer relationships (.68), Physical environment (.63), and Faculty orientation toward learning (.61). This reflects high structural cohesion, suggesting that the pedagogical relationship is the core that defines the student’s global perception of the environment.

The second factor, Negative Interactions (FE2), integrates Avoidance of negative interactions (NA1-D3) (.99) and Lack of recognition or praise (NA1-D4) (.71). These dimensions describe a pattern of emotional distancing and absence of faculty encouragement.

The third factor, Faculty Expectations (FE3), is composed of Promotion of student participation and involvement (NA1-D1) (.86) and Personal consideration toward the student (.72). This construct reflects the support, trust, and motivation that the faculty member projects.

Based on the analysis of the maximum loadings (.96 for interaction and .99 for avoidance), it is evidenced that behavioral and affective components are the central markers of the student experience.

3.2 Determinants of academic achievement: Regression models

The relationship between faculty- and student-level factors and academic achievement is fundamental to understanding how perceptions and beliefs influence student performance. Regression analysis allows for the estimation of the specific weight of each identified factor.

To analyze the joint effects, a model with six variables was initially fitted ( Table 1). However, preliminary multicollinearity analysis revealed significant statistical redundancy among the faculty-level factors. Furthermore, substantial shared variance was evidenced between Theories of Intelligence (FD1) and Perception of Potential (FD3) with Faculty Expectations (FD2). This suggests that explicit expectations (FD2) act as the integrating construct that crystallizes underlying beliefs. Consequently, considering model parsimony and estimation stability, FD2 was retained as the main predictor.

Table 1. Results of the multiple linear regression model: Student and faculty factors.

Variable	Coefficient (B)	Standard error	t	p
(Intercept)	0.08	1.22	0.06	0.95
Student Level (NA)
FE1 Classroom Climate	0.1	0.05	2.22	.027*
FE2 Negative Interactions	0.09	0.05	1.67	0.095
FE3 Perceived Expectations	0.15	0.07	2.07	.039*
Faculty Level (ND)
FD2 Faculty Expectations	1.18	0.22	5.31	< .001***

* p < .05.

*** p < .001. Variables FD1 (Theories) and FD3 (Potential) were excluded from the final model based on parsimony criteria and statistical collinearity.

Table 2 presents the fit of the hierarchical multilevel model. At Level 1 (Student), it is observed that FE1 Classroom Climate has a positive and significant effect (B = 0.10; p < .05), implying that as the perception of the environment improves, average performance increases. FE2 Negative Interactions shows a positive coefficient (B = 0.09), although its significance level (p = .092) suggests a marginal effect. Regarding FE3 Student-Perceived Faculty Expectations (p = .055) results indicate a favorable positive trend.

Table 2. Hierarchical multilevel regression model.

Fixed effects	Coefficient (B)	Robust SE	t	p
(Intercept)	0.08	1.49	0.05	0.959
Level 1: Student
FE1 Classroom Climate	0.1	0.05	2.04	.041*
FE2 Negative Interactions	0.09	0.05	1.69	0.092
FE3 Perceived Expectations	0.15	0.08	1.92	0.055
Level 2: Faculty
FD2 Faculty Expectations	1.18	0.26	4.5	< .001***

* p < .05,

*** p < .001.

At the Faculty Level, FD2 Faculty Expectations presents a strong coefficient (B = 1.18; p < .001). This indicates that faculty expectations constitute the most relevant predictor of academic achievement, surpassing individual variables. This finding provides solid evidence of the Pygmalion effect in university environments.

This allows for generating the equation:

(2)

Y_{ij} = 0.08 + 0.10 (FE 1_{ij}) + 0.09 (FE 2_{ij}) + 0.15 (FE 3_{ij}) + 1.18 (FD 2_{j}) + ε_{ij}

Where:

$Y_{ij}$ : estimated academic average of student i with faculty member j.

$0.08$ : global model intercept.

$FE 1_{ij}$ : perceived classroom climate (NA).

$FE 2_{ij}$ : negative interactions (NA).

$FE 3_{ij}$ : perceived expectations (NA).

$FD 2_{j}$ : faculty expectations (ND).

$ε_{ij}$ : residual random error.

The coefficient associated with FE1 (0.10) indicates that for each unit increase in positive climate perception, a 0.10-point increase in the average is expected. The coefficient corresponding to FD2 (1.18) suggests that a one-unit increase in faculty expectations is associated with a 1.18-point increase in the group’s average academic achievement. This effect was statistically significant (p < .001), conferring high inferential robustness.

4. Discussion

The present research, explanatory in design and utilizing a quantitative approach, allowed for analyzing the manifestation of the Pygmalion effect in the university context through a multilevel model, integrating faculty and student perceptions. The Pygmalion effect, understood as the influence expectations exert on performance, was operationalized using five validated psychometric instruments (TEQ, ITIS, SPPS, SPED, and CCS), with adequate indicators of content validity (Aiken’s V > 0.85) and internal reliability (Cronbach’s α > 0.79). The exploratory factor analysis allowed for identifying three structural factors at both the faculty level (ND) and the student level (NA), explaining 59.20% and 69.23% of the total variance, respectively.

Classroom climate constitutes an essential structural component of the school environment, with direct implications for the academic, emotional, and social development of the university student body. Regarding this, Van der Sijde and Tomic (1992) point out that faculty behavior significantly impacts students’ climatic perception. For their part, Wikman et al. (2024) indicate that this psychosocial space is one of the main contexts for development and well-being protection. This is corroborated by the results of this study, where the Classroom Climate factor (FE1) had a significant positive effect on academic achievement (B = 0.10; p = .041), being consistent with Ramírez Hernández et al. (2024), who highlight classroom climate, along with eudaimonic well-being, in explaining a large part of student engagement.

These findings have relevant clinical implications from a public health and university well-being perspective. The confirmation that classroom climate predicts academic achievement suggests that the pedagogical environment acts as a social determinant of mental health. A growing psychosocial risk factor caused by an inadequate classroom climate or low expectations not only depresses academic achievement but can also be capable of exacerbating anxiety and stress conditions. Therefore, faculty interventions should not deal only with didactic strategies, but must contemplate mental health-promoting mechanisms that diminish student burnout.

Negative Interactions (FE2) prove relevant, as empirical evidence demonstrates. This factor describes the absence of recognition and emotional support, being associated with an affectively impoverished environment that may limit or reduce performance. Although its statistical effect was lower (B = 0.09; p = .092), its latent impact suggests important risks. Hafen et al. (2015) warn that negative faculty perceptions toward disruptive students are persistent; furthermore, Timmermans and Rubie-Davies (2023) point out that disadvantaged groups are susceptible to these effects to a greater extent. The absence of praise generates frustration and deviant behaviors, which reinforces the importance of mitigating these interactions (Letuma, 2025).

A favorable trend toward achievement was observed (B = 0.15; p = .055) regarding Perceived Faculty Expectations (FE3). Meichenbaum et al. (1969) and Karabenick (1994) demonstrated that induced expectations and perceived support foster motivation and capability, being consistent with the findings of this study. For their part, Yusupova et al. (2022) state that these expectations can be influenced by prior performance, creating a feedback loop.

Regarding the faculty level, Theories of Intelligence (FD1) showed that the fixed view had a dominant loading (λ = 1.00). As Tao et al. (2021) point out, this is critical, as a fixed view diminishes faculty motivation to intervene pedagogically. Conversely, Zhang and He (2025) indicate that the belief that intelligence can be developed (malleable view) is associated with better outcomes, especially in favorable environments. Additionally, Perception of Potential (FD3)—which explains 30.08% of the variance in the EFA—correlated significantly with this malleable view ( $r = 0.64$ ; p < .001). Although FD3 was excluded from the predictive model due to collinearity, authors such as Alsultan et al. (2024), Camiel et al. (2017), and Loucif et al. (2020) suggest that faculty members with growth beliefs tend to better personalize academic trajectories and value student capabilities.

Among the distinct factors analyzed, Faculty Expectations (FD2) stood out due to its predictive robustness regarding academic achievement (B = 1.18; p < .001), surpassing the influence of student perceptions. Regarding this, Brophy (1983), Rubie-Davies et al. (2006), and Wang et al. (2018) corroborate that faculty beliefs directly impact educational attainment. Furthermore, the strong correlation between emotional climate, feedback, and expectations ( $r = .55 - .62$ ) suggests that high expectations do not operate in a vacuum, but rather in supportive and challenging pedagogical environments.

A key question is whether these expectations operate unconsciously. The results suggest that this occurs, at least in part, insofar as they are influenced by underlying beliefs regarding intelligence and manifest in concrete faculty practices such as emotional tone and feedback quality. Likewise, results indicate that it is not strictly necessary for the student to be conscious of these expectations for the Pygmalion effect to occur; faculty attitude (FD2) proved to be more determinant than student perception (FE3). Consequently, the effect can happen provided the faculty member acts coherently based on their positive expectations.

This study supports the formulation of proposals at three levels from an applied perspective: a) In faculty training, recommending the inclusion of content on biases and non-explicit beliefs. b) In pedagogical interventions, seeking to enhance positive recognition and avoid negative interactions. c) In institutional policies, suggesting the design of monitoring tools that evaluate faculty expectations as early indicators of academic risk.

It must be considered that this study has certain limitations that should be recognized for an adequate interpretation of the findings. Given it is a cross-sectional design, establishing strict causal relationships is not possible; although regression models suggest directionality, bidirectionality cannot be ruled out. The use of self-report measures may entail social desirability biases. Therefore, future lines of research should incorporate direct observational measures of classroom interaction and longitudinal designs that evaluate the sustainability of the Pygmalion effect throughout the university trajectory.

Regarding the multilevel structure of the design, it is important to note that the number of Level 2 units (n = 30 faculty members) is somewhat below the sample sizes typically recommended for obtaining reliable and stable parameter estimates at the group level in multilevel models. Although this does not invalidate the overall findings —since the Level 1 student sample is robust and the Level 2 estimates are consistent with theoretical expectations— readers should interpret the faculty-level coefficients with appropriate caution, and future studies in larger institutional contexts would strengthen the generalizability of these results. Additionally, although the effects associated with Negative Interactions (FE2; p = .092) and Student-Perceived Faculty Expectations (FE3; p = .055) did not reach conventional levels of statistical significance (p < .05), these trends are theoretically meaningful and consistent with an extensive body of research linking affective environments and perceived support to academic achievement (Hafen et al., 2015; Meichenbaum et al., 1969; Timmermans & Rubie-Davies, 2023). Their inclusion in the model is therefore justified on theoretical grounds, and they should be interpreted as preliminary evidence warranting replication rather than null findings.

However, these methodological limitations do not detract validity from the evidence presented, as it constitutes a robust and necessary empirical contribution in the Latin American university context. This justifies the implementation of pedagogical strategies focused on faculty expectation management.

5. Conclusions

The present research allowed for modeling the latent structure of the Pygmalion effect in higher education, identifying two hierarchical levels of influence. At the Student Level, three dimensions were identified: Classroom Climate (FE1), Negative Interactions (FE2), and Perceived Expectations (FE3); while at the Faculty Level, Faculty Expectations (FD2), Implicit Theories (FD1), and Perception of Potential (FD3) were validated.

One of the study’s most relevant findings is the primacy of Faculty Expectations (FD2) as the most robust determinant of academic achievement, significantly surpassing student perception variables. Thus, it is empirically confirmed that the Pygmalion effect does not operate as an isolated belief in university contexts, but as a systemic construction where practices such as emotional climate, feedback, and level of demand interact to shape a learning environment that conditions achievement. The model suggests that when practices are coherently articulated by the faculty member, a cumulative effect is produced that enhances group performance.

A theoretical and statistical connection between faculty beliefs and their pedagogical judgment was satisfactorily established. Professors who tend to perceive greater potential in their students are those who hold a malleable or incremental view of intelligence. On the other hand, at the student level, it was evidenced that classroom climate acts as a protective and stimulating factor; however, its impact depends largely on faculty expectations. This validates the multilevel nature of the phenomenon.

It is imperative to implement institutional policies that intervene in classroom culture, restructuring faculty beliefs and attitudes through targeted professional development, thus transcending the traditional approach of technical training. These interventions—aimed at raising faculty expectations—would have a multiplier effect on educational quality, even more so in the first semesters of training, as this is a critical stage where these expectations possess greater academic transformational capacity.

Ethical considerations

The study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of the Universidad Técnica del Norte (Protocol code: UTN-FECYT-CEI-2025-003-C; Date of approval: 13 January 2025). Written informed consent was obtained from all subjects involved in the study via electronic forms to participate and for the use of their anonymized data for research purposes. All participating students were legally adults (18 years or older) at the time of data collection. Therefore, no minors were involved in this study and no guardian consent was required.

Data availability

Underlying data

Figshare: Minimal Dataset and Instruments – Pygmalion Effect in Educational Settings (UTN, 2025).

https://doi.org/10.6084/m9.figshare.31390881 [Andrade-Ubidia, et al., 2025a].

This project contains the following underlying data (researchers seeking complete item-specific information for replication purposes are directed to the FigShare repository):

• Anonymized dataset (English-translated version)
• Psychometric instruments
• Codebook and variable description file

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Reporting guidelines

Figshare: STROBE checklist for “Faculty expectations and academic achievement: a multilevel analysis of the Pygmalion Effect in university students”. https://doi.org/10.6084/m9.figshare.31094887 [Andrade-Ubidia et al., 2025b].

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgements

The authors express their gratitude to the faculty members who collaborated in the validation of the instruments and provided logistical support, as well as to the faculty and students who participated in the study. The backing of the research and statistical analysis team, and the institutional support from the Universidad Técnica del Norte, are also acknowledged.

During the preparation of this manuscript, the authors utilized Gemini 3.0 and ChatGPT 5.1 for linguistic revision, text clarity improvement, and alignment with editorial standards. The authors take full responsibility for the content of this publication.

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Author details Author details

¹ FECYT, Universidad Tecnica del Norte, Ibarra, Imbabura Province, Ecuador

Paúl Andrade-Ubidia
Roles: Conceptualization, Methodology, Supervision, Validation, Writing – Review & Editing

Carla B.Gudiño-Mejía
Roles: Conceptualization, Formal Analysis, Methodology, Resources, Software, Validation, Writing – Original Draft Preparation

Nelson Salazar
Roles: Data Curation, Visualization, Writing – Review & Editing

Evelyn Hernández-Martínez
Roles: Data Curation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

Universidad Técnica del Norte [grant number UTN-2024-FH-EDU-015]
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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https://doi.org/10.12688/f1000research.177579.2

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Andrade-Ubidia P, B.Gudiño-Mejía C, Salazar N and Hernández-Martínez E. Faculty expectations and academic achievement: a multilevel analysis of the Pygmalion Effect in university students [version 2; peer review: 1 approved]. F1000Research 2026, 15:413 (https://doi.org/10.12688/f1000research.177579.2)

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Reviewer Report 14 May 2026

Eric Guerra-Dávila, Instituto Superior Tecnológico ITCA, Ibarra, Ecuador

Approved

https://doi.org/10.5256/f1000research.200427.r481775

I have reviewed the new version of the paper as a result of my comments. In each case, the authors have adequately responded to the points that I had raised about this work.
The added specification in Section 2.3 ... Continue reading

I have reviewed the new version of the paper as a result of my comments. In each case, the authors have adequately responded to the points that I had raised about this work.
The added specification in Section 2.3 on how the authors excluded factors from their analysis based upon their loadings (λ < .40) along with the addition of references to these specifications in the 'Results' section will be sufficient for providing the requested methodological clarity. The enhanced description of limitations has provided an accurate reflection of the constraints of having a small number of schools (n=30) at the level 2 unit of observation and properly cautions readers regarding interpreting the results at the faculty level of analysis.
Additionally, in regard to the marginal effects of FE2 and FE3, presenting them as theoretical trends as opposed to statistical evidence is both reasonable and consistent with the current literature. The reference to FigShare for details on each individual item was sufficient to address concerns of replicability.
There were no modifications made to either the study's methodology, data, analysis techniques, or findings. This is proper since it appears the original findings were valid. All changes made to the study are strictly editorial and provide increased interpretative clarity; however, they do not alter the scientific contribution of the paper.

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Educational research; higher education pedagogy; didactics and teaching practices; science education; systematic reviews in education; ICT in education; teacher training.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Eric Guerra-Dávila, Instituto Superior Tecnológico ITCA, Ibarra, Ecuador

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https://doi.org/10.5256/f1000research.195818.r471393

Presentation and Literature (Q1 — Yes):
The paper was well-written and easy to follow. The literature was up-to-date, and both foundational (Rosenthal & Jacobson, 1968; Brophy, 1983) and contemporary studies were included (Rubie-Davies & Hattie, 2024; Zhang & He, ... Continue reading

Presentation and Literature (Q1 — Yes):
The paper was well-written and easy to follow. The literature was up-to-date, and both foundational (Rosenthal & Jacobson, 1968; Brophy, 1983) and contemporary studies were included (Rubie-Davies & Hattie, 2024; Zhang & He, 2025). The paper followed APA 7 format consistently.

Design Study and Technical Quality (Q2 — Partly):
Although the cross-sectional, multi-level design was appropriate and reasonable for achieving the goals of this study, the authors might want to include a small notation in the Limitations Section concerning the number of participants at Level 2 (n=30 Faculty), which is somewhat below the typical number of participants needed at each level for reliable, accurate, multilevel parameter estimates. Although this will not diminish the overall validity of the study, including this statement will improve the overall interpretative quality of the study.

Replication Methodology (Q3 — Partly):
For the purposes of this study, the methodology has been adequately detailed. I was pleased that the authors made the raw data set and survey instruments available through FigShare for open access to interested researchers. In addition to providing clear instructions for accessing these materials, the authors should provide a quick reference to direct interested parties to this location if they need the complete item-specific information for further replication. Finally, a very small amount of additional text would be necessary to describe the method used to eliminate some items from inclusion in the exploratory factor analyses (EFA). Specifically, the authors can explain what percentage of variance (loadings) had to be eliminated for those items to be excluded. For example: "Items D1-D4 were removed due to loadings less than 0.40."

Statistical Analysis (Q4 — Partly):
The statistical techniques used were appropriate. Use of MinRes for principal component extraction using Varimax rotation and use of Horn's parallel analysis to determine how many factors to retain from the initial factor solution were good choices for analyzing this type of data. Use of robust standard error in the multilevel model was also a good choice. My suggestions for improving the clarity of the reporting are minor and intended to support additional interpretive transparency and credibility rather than change anything about the findings.

As noted by the authors, FE2 and FE3 indicate marginally significant or trend level effects (p=.092 and p=.055 respectively). Although statistically significant effect sizes require a probability of less than .05 according to most statistical guidelines for determining the presence of real effects, it is understandable that some effects that are theoretically important may still fall short of meeting these stringent statistical requirements. Therefore, I suggest that in the Limitation Section, you consider adding a few sentences that frame your interpretation of these trends as being supported by theory rather than solely based upon statistical tests. This is entirely consistent with the way in which your results have already been framed and no changes to the reported findings will be required.

In addition to flagging the absence of an adjusted R squared in Table 1, you could optionally report any of the fit statistics available for your multilevel model (e.g., Akaike Information Criteria or -2 Log Likelihood) in Table 2. This would enable you to facilitate comparisons among other multilevel studies even though it is not strictly required.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Educational research; higher education pedagogy; didactics and teaching practices; science education; systematic reviews in education; ICT in education; teacher training.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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I have reviewed the new version of the paper as a result of my comments. In each case, the authors have adequately responded to the points that I had raised about this work.
The added specification in Section 2.3 on how the authors excluded factors from their analysis based upon their loadings (λ < .40) along with the addition of references to these specifications in the 'Results' section will be sufficient for providing the requested methodological clarity. The enhanced description of limitations has provided an accurate reflection of the constraints of having a small number of schools (n=30) at the level 2 unit of observation and properly cautions readers regarding interpreting the results at the faculty level of analysis.
Additionally, in regard to the marginal effects of FE2 and FE3, presenting them as theoretical trends as opposed to statistical evidence is both reasonable and consistent with the current literature. The reference to FigShare for details on each individual item was sufficient to address concerns of replicability.
There were no modifications made to either the study's methodology, data, analysis techniques, or findings. This is proper since it appears the original findings were valid. All changes made to the study are strictly editorial and provide increased interpretative clarity; however, they do not alter the scientific contribution of the paper.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Educational research; higher education pedagogy; didactics and teaching practices; science education; systematic reviews in education; ICT in education; teacher training.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Eric Guerra-Dávila, Instituto Superior Tecnológico ITCA, Ibarra, Ecuador

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Approved With Reservations

Presentation and Literature (Q1 — Yes):
The paper was well-written and easy to follow. The literature was up-to-date, and both foundational (Rosenthal & Jacobson, 1968; Brophy, 1983) and contemporary studies were included (Rubie-Davies & Hattie, 2024; Zhang & He, 2025). The paper followed APA 7 format consistently.

Design Study and Technical Quality (Q2 — Partly):
Although the cross-sectional, multi-level design was appropriate and reasonable for achieving the goals of this study, the authors might want to include a small notation in the Limitations Section concerning the number of participants at Level 2 (n=30 Faculty), which is somewhat below the typical number of participants needed at each level for reliable, accurate, multilevel parameter estimates. Although this will not diminish the overall validity of the study, including this statement will improve the overall interpretative quality of the study.

Replication Methodology (Q3 — Partly):
For the purposes of this study, the methodology has been adequately detailed. I was pleased that the authors made the raw data set and survey instruments available through FigShare for open access to interested researchers. In addition to providing clear instructions for accessing these materials, the authors should provide a quick reference to direct interested parties to this location if they need the complete item-specific information for further replication. Finally, a very small amount of additional text would be necessary to describe the method used to eliminate some items from inclusion in the exploratory factor analyses (EFA). Specifically, the authors can explain what percentage of variance (loadings) had to be eliminated for those items to be excluded. For example: "Items D1-D4 were removed due to loadings less than 0.40."

Statistical Analysis (Q4 — Partly):
The statistical techniques used were appropriate. Use of MinRes for principal component extraction using Varimax rotation and use of Horn's parallel analysis to determine how many factors to retain from the initial factor solution were good choices for analyzing this type of data. Use of robust standard error in the multilevel model was also a good choice. My suggestions for improving the clarity of the reporting are minor and intended to support additional interpretive transparency and credibility rather than change anything about the findings.

As noted by the authors, FE2 and FE3 indicate marginally significant or trend level effects (p=.092 and p=.055 respectively). Although statistically significant effect sizes require a probability of less than .05 according to most statistical guidelines for determining the presence of real effects, it is understandable that some effects that are theoretically important may still fall short of meeting these stringent statistical requirements. Therefore, I suggest that in the Limitation Section, you consider adding a few sentences that frame your interpretation of these trends as being supported by theory rather than solely based upon statistical tests. This is entirely consistent with the way in which your results have already been framed and no changes to the reported findings will be required.

In addition to flagging the absence of an adjusted R squared in Table 1, you could optionally report any of the fit statistics available for your multilevel model (e.g., Akaike Information Criteria or -2 Log Likelihood) in Table 2. This would enable you to facilitate comparisons among other multilevel studies even though it is not strictly required.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Educational research; higher education pedagogy; didactics and teaching practices; science education; systematic reviews in education; ICT in education; teacher training.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Faculty expectations and academic achievement: a multilevel analysis of the Pygmalion Effect in university students

Abstract

Background

Methods

Results

Conclusions

Keywords

Revised Amendments from Version 1

1. Introduction

2. Methods

2.1 Instruments

Figure 1. Structure of instruments, dimensions, and items applied by variable level (ND and NA).

2.2 Analysis of instrument reliability and validity

2.3 Exploratory Factor Analysis (EFA)

2.4 Multilevel regression analysis

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3. Results

3.1 Descriptive analysis and factor structure

Figure 2. Spearman correlation matrix, distributions, and scatterplots among faculty-level dimensions.

Figure 3. Spearman correlation matrix, distributions, and scatterplots among student-level dimensions.

Figure 4. Exploratory factor model and standardized loadings of the faculty level (ND).

Figure 5. Exploratory factor model and standardized loadings of the student level (NA).

3.2 Determinants of academic achievement: Regression models

Table 1. Results of the multiple linear regression model: Student and faculty factors.

Table 2. Hierarchical multilevel regression model.

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4. Discussion

5. Conclusions

Ethical considerations

Data availability

Underlying data

Reporting guidelines

Acknowledgements

References

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