Achievement Goals and Income Inequality: How Inequality Weakens the Positive Association Between Mastery-Approach Goals and Achievement

Abstract

The achievement goal perspective highlights the importance of mastery-approach goals in facilitating students’ academic achievement. However, a socio-ecological lens would suggest that the effectiveness of these mastery-approach goals might vary across different levels of income inequality, specifically national- and school-level income inequality. Understanding the role of income inequality is essential for advancing a socio-ecologically sensitive achievement goal approach. This study examines whether the association between mastery-approach goals and academic achievement is moderated by national- and school-level income inequality. Our hypothesis is that the adaptive benefits associated with mastery-approach goals are weaker in socio-ecological contexts with higher national- and school-level income inequality. The study involved 273,269 students nested in 10,785 schools from 33 countries. The results revealed that both national- and school-level income inequality significantly moderated the associations between mastery-approach goals and academic achievement in the three focal subjects of mathematics, reading, and science. Both national- and school-level income inequality weakened the positive role of mastery-approach goals, and this was especially pronounced for lower socioeconomic status students. These findings underscore the importance of considering socio-ecological contexts when evaluating the impact of mastery-approach goals on academic outcomes, highlighting the need to address both achievement goals and broader socio-ecological contexts to support academic success for all students.

Keywords

achievement goals mastery-approach goals income inequality academic achievement socio-ecological contexts socioeconomic status (SES)

Introduction

Achievement goals play a central role in student motivation (Dweck, 1986; Elliot, 1997; Nicholls, 1984). Mastery-approach goals, which focus on mastering tasks and improving one's competence relative to intrapersonal standards, are important positive predictors of favorable academic outcomes, including higher levels of academic achievement and intrinsic motivation (Hulleman et al., 2010; Van Yperen et al., 2014). Although past studies have mostly examined the direct association between mastery-approach goals and academic outcomes, there is also an increasing recognition that the effectiveness of achievement goals, including mastery-approach goals, might depend on the socio-ecological context (Dekker & Fischer, 2008; King, 2022; Liem & Elliot, 2018; Zusho & Clayton, 2011)

One critical socio-ecological context is income inequality (Wilkinson & Pickett, 2021). Recent data indicate that the top 1% possess over 30% of total global wealth, while billions of individuals face economic setbacks (Oxfam, 2024). In settings with high levels of inequality, students lack access to sufficient educational resources and supportive school environments, making it more challenging to succeed academically (Berkowitz et al., 2017; Chiu, 2010). Meanwhile, inequality also shapes students’ daily experiences and perceptions. High income inequality can heighten social distance and reduce feelings of school belonging, which can undermine students’ achievement (Claes et al., 2024; King et al., 2022; Willis et al., 2022). These structural and psychological barriers together may diminish the effectiveness of mastery-approach goals, limiting students’ capacity to translate motivational strivings into actual performance.

On the other hand, in environments with lower income inequality, where resources and support are more evenly distributed (Browman et al., 2019; Du et al., 2024), mastery-approach goals may be more likely to promote academic achievement. By examining income inequality through the lens of achievement goal theory, we can gain insights into how socio-ecological factors can impede or facilitate students’ engagement, effort, and eventual achievement. Furthermore, such work promises to expand achievement goal theory's explanatory power beyond individual motivation, highlighting the differential benefits of mastery-approach goals in varying socioeconomic contexts (King et al., 2024a).

The present study investigates the associations between income inequality, mastery-approach goals, and academic achievement in three core subjects (mathematics, reading, and science). Of central interest, we test whether the links between mastery-approach goals and achievement in these domains are moderated by income inequality at both the national and school levels.

Achievement Goals

Achievement goals represent the pursuit of competence according to an evaluative standard (Dweck & Leggett, 1988; Elliot & Thrash, 2001). Elliot (1999) proposed the 2 × 2 achievement goal framework that differentiates goals according to two basic dimensions – how competence is defined and how it is valenced (see also Elliot & McGregor, 2001; Pintrich, 2000) Individuals may define competence in terms of inherent task demands or their previous performance (mastery goal), or they may define competence in terms of their performance relative to others (performance goal; Dweck, 1986). Furthermore, individuals may conceptualize competence in terms of a successful outcome they desire (approach goal) or an unsuccessful outcome they fear (avoidance goal; Elliot & Harackiewicz, 1996) Thus, the 2 × 2 framework identifies four types of achievement goals: Mastery-approach, performance-approach, mastery-avoidance, and performance-avoidance¹. The present study focuses exclusively on mastery-approach goals (the only type of goal assessed in the PISA 2018 that served as the basis for this research).

Mastery-Approach Goals and Academic Achievement

Achievement goals are associated with distinct affective, cognitive, and behavioral processes that have an important influence on outcomes relevant to competence (Elliot & Hulleman, 2017; Huang, 2011; Pekrun et al., 2009). Among those goals, mastery-approach goals are particularly likely to facilitate deeper engagement in learning activities, resilience when facing setbacks, and higher levels of intrinsic interest in the subject matter (Huang, 2016; Hulleman et al., 2010). Most pertinent to the present research, numerous studies have documented a positive relationship between mastery-approach goals and students’ test scores and academic performance (Guo et al., 2023; Katz-Vago & Benita, 2024; King et al., 2024b; Mouratidis et al., 2018).

Although the effect sizes vary across studies (e.g., Cellar et al., 2011; Huang, 2012; Van Yperen et al., 2014), in general, meta-analytic evidence suggests that mastery-approach goals are positively associated with academic performance. For example, Payne et al. (2007) found that learning goals were positively associated with academic performance (r = .12). Similarly, Baranik et al. (2010) and Cellar et al. (2011) documented positive associations between mastery-approach goals and performance outcomes with medium effects (r_s = .10 and .22, respectively). Huang's (2012) meta-analysis reported consistent positive correlations between mastery-approach goals and achievement (r = .10). Wirthwein et al. (2013) concluded that mastery-approach goals are significantly related to various achievement indicators (mean effect sizes from .11 to .18). Van Yperen et al. (2014) extended this analysis, finding that mastery-approach goals lead to better performance (r = .14)². However, the specific associations between mastery-approach goals and achievement in particular domains remain under-researched.

In the present study, we attend to the associations of mastery-approach goals and academic achievement in three core academic subjects: Mathematics, reading, and science. These subjects represent distinct sets of knowledge and thinking skills, and are commonly featured in large-scale evaluations of student performance (Levin, 2013). Mastery-approach goals have been linked to benefits in these domains, including higher scores on math assessments, improved reading comprehension, and higher science achievement (Belenky & Nokes-Malach, 2012, 2013; Mouratidis et al., 2018). However, little research has been conducted that includes these three indicators of achievement within the same investigation. Based on the literature reviewed, we propose the following hypothesis:

Hypothesis 1 (H1): Mastery-approach goals are positively associated with (a) mathematics, (b) reading, (c) and science achievement.

Income Inequality and Academic Achievement

Income inequality refers to the uneven sharing of economic resources in a population, leading to a gap between high-income and low-income groups (Corak, 2013; Topuz, 2022). In the last few decades, this gap has widened globally, prompting researchers to examine its impact on social and individual outcomes, including educational outcomes (King et al., 2022; Workman, 2023). Viewed as a socio-ecological factor, income inequality underscores how broader structural conditions extend beyond individual-level differences (Layte, 2012; Sánchez-Rodríguez et al., 2019). These conditions of inequality significantly restrict students’ learning resources, hindering the achievement of learners from disadvantaged backgrounds (Berkowitz et al., 2017; Chiu, 2010).

Empirical evidence links income inequality with lower academic achievement (Chiu & Chow, 2015; Condron, 2011). For instance, research has revealed that in countries with higher inequality, students tend to display lower scores on tests and standardized assessments (Du et al., 2024; King et al., 2024a; Yan & Chiu, 2023). Disadvantaged learners in these settings may feel that future financial security is out of reach, potentially reducing their academic engagement (Sommet et al., 2024). Such dynamics create a context where students from low-income families face additional hurdles, including fewer high-quality resources and less encouraging educational environments (Browman et al., 2019; Chiu, 2010; García-Sánchez et al., 2024).

Despite an emerging interest in how income inequality is associated with academic achievement, little research has addressed whether this association varies as a function of the type or level of income inequality. By exploring both national-level and school-level income inequality, the present study seeks to acquire a more nuanced picture of the link between income inequality and academic achievement.

National-Level and School-Level Income Inequality

National-level income inequality pertains to income gaps between families across an entire nation, reflecting the extent to which economic resources are concentrated or evenly dispersed in a country's population (Solt, 2016). School-level income inequality represents gaps between families with high and low incomes within a school, reflecting the extent to which economic resources are concentrated or evenly dispersed among students who attend the same school (Menzer & Torney-Purta, 2012). Both forms of inequality have the potential to affect students’ educational experiences (e.g., Chiu, 2010; 2017; King et al., 2022).

At the national level, larger income inequality between wealthy and less affluent families can set the overall policy climate and resource allocation for public education (Brian, 2015; Chiu & McBride-Chang, 2010; Rodriguez-Bailon et al., 2017). When national-level income inequality is high, students from disadvantaged backgrounds may have less access to necessary support, such as sufficient school funding, well-trained teachers, and adequate learning. Several studies have indeed documented negative associations between national-level income inequality and academic achievement. For instance, Chiu and Chow (2015) found that national-level income inequality undermined reading achievement, and similar findings have emerged for mathematics and science achievement (King et al., 2024b; Wang & Wu, 2023). While these structural explanations are critical, income inequality may also operate at the psychological level. For example, Claes et al. (2024) demonstrated that national-level income inequality exacerbated students’ academic anxiety, which widened performance gaps between social classes across countries. This evidence highlights that the negative consequences of national-level income inequality are not limited to structural deprivation, but extend to psychological processes.

At the school level, income inequality is experienced more directly in students’ daily interactions with peers. Larger income gaps within schools can heighten perceptions of relative deprivation and intensify social comparisons, leading to lower belonging for disadvantaged students (King et al., 2022; Sommet et al., 2023; Willis et al., 2022). Moreover, school-level inequality has been linked to maladaptive outcomes such as aggression and bullying (Due et al., 2009; Menzer & Torney-Purta, 2012). These dynamics can create an environment that undermines collaborative learning and peer support, thereby reducing overall achievement, not only for disadvantaged groups but also for the broader student body within the school (Berkowitz et al., 2017; Marks, 2006; Owens et al., 2016). In contrast, more equitable schools tend to foster resource sharing and inclusive peer climates, which could support higher levels of achievement (Berkowitz et al., 2017; Marks et al., 2006; Owens, 2018).

Taken together, these findings suggest that income inequality at both the national and school levels constrains students’ achievement through a combination of structural and psychological mechanisms. Aligning with prior evidence that reduced inequality is often associated with improved educational outcomes (Broer et al., 2019; Du, 2024; Wilkinson & Pickett, 2009), the present study aims to investigate how both national- and school-level income inequality are associated with academic achievement. Understanding these dual layers of income inequality can clarify whether national socioeconomic conditions, more local socioeconomic conditions, or both influence variation in students’ academic achievement. We propose the following hypotheses:

Hypothesis 2 (H2): National-level income inequality is negatively associated with (a) mathematics, (b) reading, and (c) science achievement;

Hypothesis 3 (H3): School-level income inequality is negatively associated with (a) mathematics, (b) reading, and (c) science achievement.

The Moderating Role of Income Inequality

Previous research has revealed considerable variability in the association between mastery-approach goals and achievement, and meta-analyses have highlighted notable variability in effect sizes (e.g., Baranik et al., 2010; Huang, 2012; Hulleman et al., 2010; Van Yperen et al., 2014). Some meta-analyses have reported mastery-approach goals as positive predictors of performance (Huang, 2012; Van Yperen et al., 2014), while others found more inconsistent effect sizes depending on contextual factors (Burnette et al., 2013; Hulleman et al., 2010). This inconsistency reveals the presence of hidden contextual moderators that may amplify or constrain the benefits of mastery-approach goals. Beyond direct associations with academic achievement, national- and school-level income inequality may moderate the associations between mastery-approach goals and academic achievement.

Income inequality shapes both the distribution of material resources and the salience of social stratification in everyday life (Solt, 2016, 2020; Wilkinson & Pickett, 2009). By considering income inequality as a socio-ecological factor, we recognize that achievement goal pursuit is inherently linked to objective realities in students’ lives (Liem & Elliot, 2018; Oishi & Choi, 2017; Urdan & Kaplan, 2020). As a socio-ecological factor, income inequality exacerbates educational stratification. It strengthens the link between family background and student opportunity, while also making school systems themselves more selective and divided (Thorson & Gearhart, 2018; Wang & Wu, 2023). These inequalities also shape the association between mastery-approach goals and academic achievement, such that the same level of mastery-approach goals may yield different performance outcomes depending on how supportive or restrictive the opportunity structure is in a given environmental context (King, 2022, 2024a; Skinner et al., 2022).

From an achievement goal perspective, incorporating a socio-ecological lens on income inequality underscores that the pursuit of competence, particularly through mastery-approach goals, is not a solitary endeavor. While achievement goal theory indicates that learners who adopt mastery-approach goals, which focus on mastering material and enhancing skills, are more likely to have higher achievement, the structural reality of underfunded schools and unequal resource allocation may restrict the extent to which students can fully utilize these goals. In environments with higher national-level income inequality, the benefits of mastery-approach goals may be diminished due to structural barriers and resource deficits (Chiu & Chow, 2015; King et al., 2024b). Students in these contexts may struggle to access educational opportunities that facilitate higher performance, undermining the link between mastery-approach goals and academic achievement. In contrast, in settings with lower national-level income inequality, where resources are more evenly distributed, mastery-approach goals may be more likely to promote academic achievement (Erentaitė & Vosylis, 2025; Erentaitė et al., 2023; Liang et al., 2024; Michael & Kyriakides, 2023).

In addition to the broader national-level considerations, the role of school-level income inequality also deserves attention. School-level income inequality leads to daily encounters with resource disparities (Chiu, 2017), thereby shaping students’ immediate learning conditions in ways that can hinder mastery-approach goals. When substantial income inequality exists among students within the same school, it is possible that students from lower-income families may feel they lack adequate support and feel like they do not belong in school (Canning et al., 2020). Experiencing income inequity within schools may also trigger anxiety and crowd out more intrinsic aspects of motivation (King et al., 2024a). These factors can hinder students’ complete engagement in the process-oriented, self-improvement focus that is integral to the benefits of mastery-approach goals (Doll, 2022; Patrick et al., 2011). Consequently, in schools with higher school-level income inequality, mastery-approach goals may be less effective in fostering academic achievement.

Given the literature on both national-level and school-level income inequality, we consider the moderating role of income inequality – at both the national and school levels – on the association between mastery-approach goals and academic achievement. We propose the following hypotheses:

Hypothesis 4 (H4): National-level income inequality weakens the positive association between mastery-approach goals and (a) mathematics, (b) reading, and (c) science achievement;

Hypothesis 5 (H5): School-level income inequality weakens the positive association between mastery-approach goals and (a) mathematics, (b) reading, and (c) science achievement.

The Moderating Role of Socioeconomic Status (SES)

Although national- and school-level inequality are expected to be negatively associated with achievement, accumulating evidence indicates that inequality is more harmful for less advantaged students (Sommet et al., 2024). This pattern is theoretically consistent with the biopsychosocial model of challenge and threat. Competitive contexts associated with inequality can elicit either threat or challenge appraisals, with consequent performance costs or benefits that are strongly shaped by the availability of resources (Blascovich, 2013; Jamieson & Elliot, 2018; Seery, 2013). Empirically, cross-national PISA analyses show that the greatest harms of inequality accrue to disadvantaged students (Chiu, 2010; Claes et al., 2024). Accordingly, we expect stronger adverse associations of inequality with achievement among lower-SES students, and propose the following hypotheses³:

Hypothesis 6 (H6): SES moderates the negative association between national-level income inequality and (a) mathematics, (b) reading, and (c) science achievement.

Hypothesis 7 (H7): SES moderates the negative association between school-level income inequality and (a) mathematics, (b) reading, and (c) science achievement.

Concurrently, income inequality not only limits the availability of resources but also exacerbates class distinctions in parental investments, such as time, tutoring, and materials (Reardon & Bischoff, 2011; Schneider et al., 2018). Consequently, students with higher SES gain the necessary out-of-school support to translate mastery goals into tangible performance, while lower-SES students might not be able to achieve comparable returns. Additionally, income segregation and organizational stratification intensify comparative cues and concentrate resources, exacerbating disadvantages for low-SES students and further diminishing the efficacy of mastery-approach goals in unequal settings (Chiu, 2010; Marks, 2006). This study seeks to further investigate whether the income inequality-based attenuation observed in the association between mastery-approach goals and academic achievement is itself SES-based.

Hypothesis 8 (H8): SES moderates the interaction between mastery-approach goals and national-level income inequality on (a) mathematics, (b) reading, and (c) science achievement.

Hypothesis 9 (H9): SES moderates the interaction between mastery-approach goals and school-level income inequality on (a) mathematics, (b) reading, and (c) science achievement.

Method

Participants

This study used the Program for International Student Assessment (PISA) 2018 data⁴ which included responses from 273,269 15-year-old adolescent students across 10,785 schools in 33 OECD countries⁵. The gender ratio was nearly equal: Females = 13,5760 (49.7%) and males = 13,7507 (50.3%). Table 1 presents country-level summaries of national inequality alongside correlations between school-level inequality, mastery-approach goals, and achievement (mathematics, reading, and science) for each participating OECD nation.

Table 1.

Correlations Among National-Level Income Inequality, School-Level Income Inequality, Mastery-Approach Goals, and Achievement in Each Nation.

			Correlations (r) between School-Level Income Inequality and Achievement			Correlations (r) between Mastery-Approach Goals and Achievement
Country	National-Level Income Inequality	Log GDP per Capita	Mathematics	Reading	Science	Mathematics	Reading	Science
Australia	32.7	4.73	−.014	.006	< .001	.165***	.147***	.126***
Austria	27.7	4.69	.021	.003	.004	.077***	.110***	.086***
Belgium	25.9	4.66	−.011	−.003	−.008	−.002	.049***	.008
Canada	30.6	4.65	.027**	.025**	.022**	.154***	.131***	.114***
Chile	45.5	4.16	.023*	.028*	.017	.052***	.029*	.019
Colombia	48.0	3.80	.045***	.034**	.037**	.020	.045***	.039***
Czech Republic	24.4	4.31	−.086***	−.096***	−.081***	.141***	.142***	.116***
Denmark	26.4	4.79	−.027*	−.026*	−.019	.128***	.182***	.132***
Estonia	30.7	4.33	−.037**	−.025	−.020	.193***	.202***	.206***
Finland	26.0	4.68	−.010	.000	−.002	.219***	.211***	.233***
France	30.1	4.61	.035**	.049***	.039**	.083***	.089***	.073***
Germany	29.7	4.68	.060***	.063***	.055***	.064***	.106***	.057***
Greece	31.1	4.28	.021	.024	.025*	.166***	.181***	.166***
Hungary	27.4	4.18	.065***	.056***	.055***	.176***	.174***	.171***
Ireland	29.1	4.77	−.133***	−.136***	−.133***	.190***	.186***	.171***
Italy	33.0	4.53	.021*	.010	.026**	.067***	.106***	.069***
Korea	33.7	4.52	−.007	−.007	−.016	.158***	.121***	.139***
Latvia	34.4	4.22	−.012	−.012	−.012	.154***	.101***	.108***
Lithuania	36.0	4.24	.027*	.019	.018	.156***	.168***	.154***
Mexico	42.6	3.98	.025*	.027*	.031**	.064***	.086***	.044***
Netherlands	27.2	4.70	−.444***	−.396***	−.443***	−.042*	−.029	−.047**
New Zealand	32.2	4.62	−.245***	−.191***	−.218***	.178***	.177***	.150***
Norway	25.8	4.91	−.023	−.022	−.017	.295***	.340***	.281***
Poland	29.2	4.15	.013	.012	.024	.133***	.174***	.157***
Portugal	31.8	4.34	−.025	−.009	−.007	.167***	.183***	.184***
Slovak Republic	22.9	4.26	−.008	−.008	−.007	.227***	.214***	.210***
Slovenia	24.3	4.39	.52***	.049***	.048***	.054***	.066***	.054***
Spain	33.0	4.47	−.036***	−.032***	−.030***	.182***	.184***	.167***
Sweden	28.4	4.75	−.023	−.020	−.020	.151***	.123***	.138***
Switzerland	30.3	4.92	−.012	−.011	−.021	.086***	.126***	.096***
Turkey	40.5	4.03	.299***	.287***	.295***	−.078***	−.059***	−.086***
United Kingdom	31.4	4.62	−.009	.023**	.001	.124***	.107***	.096***
United States	38.8	4.80	.016	.021	.016	−.029*	.004	−.039***

Notes. * p < .05; ** p < .01; *** p < .001.

Measures

National-Level Income Inequality

National-level income inequality was assessed using the Gini index from the Standardized World Income Inequality Database (Solt, 2020). The Gini index quantifies income dispersion on a scale from 0, indicating perfect equality (all individuals have the same income), to 100, denoting complete inequality (one individual possesses all income while others have none). Data for the Gini index of 33 OECD countries from the year 2018 were employed in this study (Solt, 2020).

School-Level Income Inequality

School-level income inequality was quantified by computing a school-level Gini coefficient for each school based on the dispersion of family economic, social, and cultural status (ESCS, a SES index formulated by PISA; OECD, 2019a). The Gini coefficient, commonly used to assess national-level income inequality, was applied here to the distribution of ESCS values at the school level, with a higher school-level Gini coefficient indicating greater inequality within that school. This approach has been used in prior studies that drew on the PISA dataset (e.g., Chiu, 2015; King et al., 2022).

Mastery-Approach Goals

Mastery-approach goals were measured using the index of mastery-approach goals provided in the PISA 2018 (OECD, 2019b). This index comprises three 4-point items, rated from 1 (not at all true for me) to 4 (extremely true for me). This scale was adapted and developed by the OECD based on the previously published Achievement Goal Questionnaire-Revised (Elliot & Murayama, 2008). A sample item is, “How true for you: My goal is to completely master the material presented in my classes.”

Mathematics Achievement, Reading Achievement, and Science Achievement

The PISA dataset contains achievement scores in three subjects: Mathematics, reading, and science. Ten plausible values were provided to indicate the estimates of the score the individual student might reasonably have from the assessment for each subject (OECD, 2009). Plausible values were generated using latent regression models that applied Item Response Theory (IRT) to scale cognitive data across multiple domains and incorporate background variables such as gender and participation in academic or non-academic activities as covariates. This method imputes ten proficiency values for each student, providing a range of potential scores rather than a single-point estimate for each domain, thereby acknowledging the uncertainty in a student's true achievement level (Lechner et al., 2021). These plausible values were scaled to have a mean of 500 and a standard deviation of 100 using the Rasch model PISA achievement scores, and the mean scores of ten plausible values of each subject were calculated to represent achievement in mathematics, reading, and science, respectively (OECD, 2019a).

Covariates

Participants’ gender (0 = female and 1 = male), grade level (coded 7–13 to represent Grades 7 through 13, respectively), SES (operationalized as the ESCS), and immigration status (0 = native, 1 = second-generation immigrant, and 2 = first-generation immigrant) were included as covariates. At the country level, we included Gross Domestic Product (GDP) per capita in 2018 (in U.S. dollars; World Bank, 2019), which was log-transformed for analysis (logGDP).

Data Analysis

The analyses were conducted using SPSS Version 29 and Mplus version 8.2. The incidence of missing data across all variables was minimal, peaking at 11% for mastery-approach goals. To address this, missing values were imputed using the Markov Chain Monte Carlo (MCMC) method, which is noted for its high accuracy in handling missing data, thus enabling the analysis of complex datasets (Marjoram et al., 2003).

The PISA dataset exhibited a nested structure, with students nested within schools, and schools nested within countries. To adequately account for this hierarchical configuration, three-level linear modeling was employed. This approach helps to disaggregate the residuals at the student, school, and country levels, thereby minimizing estimation biases. All student-level variables were school-mean centered, school-level variables were country-mean centered, and country-level variables were standardized to a mean of zero and a standard deviation of one. This centering strategy facilitated interpretation, reduced multicollinearity, and addressed between-country heterogeneity in the estimation of school- and student-level effects (Enders & Tofighi, 2007; Marjoram et al., 2003). Additionally, final student sampling weights were applied to address sampling errors and allow for population-level inferences (OECD, 2019a).

The analytical framework adopted for this study utilized a sequential modeling approach to rigorously test the hypotheses through a series of progressively complex models (Raudenbush & Bryk, 2002). Initially, null models were constructed as an unconditional model, incorporating only an intercept to ascertain the proportion of variance in mathematics, reading, and science achievement attributable to factors at the student, school, and country levels. This was quantified using the intraclass correlation coefficient (ICC).

Subsequently, we progressed to Model 1 (M1), which encompassed multilevel regression models developed specifically to explore whether mastery-approach goals, SES, school-, and national-level income inequality are associated with mathematics (M1a), reading (M1b), and science achievement (M1c). Building on this, Model 2 (M2) extended M1 by incorporating cross-level interactions between national-level inequality and mastery-approach goals, as well as school-level inequality and mastery-approach goals, to examine whether associations between mastery-approach goals and achievement were moderated by the levels of income inequality at the national and school levels. M2 also further examined whether SES moderates the associations of national- and school-level income inequality with achievement, as well as whether SES further moderates the cross-level interactions between mastery-approach goals and national-/school-level inequality.

To enhance the robustness of the findings and account for potential confounding variables, a multilevel moderation model with covariates (M3) was employed. This step further refined M2 by controlling variables at the student (including grade, gender, and immigration status) and country (log GDP) levels, providing a more comprehensive analysis of the hypothesized associations.

Results

Preliminary Analyses

The overall correlations for the whole sample are presented in Table 2, and the correlations specific to each country are detailed in Table 1. The scatterplots show how national-level (Figure 1) and school-level (Figure 2) income inequality is associated with achievement in mathematics, reading, and science.

Figure 1.

National-level income inequality weakens the association between mastery-approach goals and (a) mathematics achievement, (b) reading achievement, and (c) science achievement.

Figure 2.

School-level income inequality weakens the association between mastery-approach goals and (a) mathematics achievement, (b) reading achievement, and (c) science achievement.

Table 2.

Descriptive Statistics and Correlations among the Variables.

	1	2	3	4	5	6	7	8	9	10	11
1. National-level income inequality	1
2. GDP	−.574***	1
2. School-level income inequality	.204***	−.099***	1
3. Mastery-approach goals	.079***	.001	.002	1
4. Mathematics achievement	−.244***	.203***	−.224***	.089***	1
5. Reading achievement	−.130***	.152***	−.192***	.118***	.858***	1
6. Science achievement	−.154***	.155***	−.199***	.092***	.900***	.910***	1
7. Grade	−.113**	.111***	−.017**	.001	.014***	−.009**	−.004	1
8. Gender	−.006***	.008***	.004*	−.099***	.040***	−.144***	−.004	.005*	1
9. SES	−.242***	.301***	−.275***	.090***	.428***	.373***	.393***	−.006**	.002	1
10. Immigration status	−.070***	.198***	.068***	.041***	−.074***	−.080***	−.088***	.022***	.004	−.058***	1
M	31.87		.79	3.41	489.87	488.44	489.99	12.25	.50	−.02	1.19
SD	5.39		.18	.96	86.98	97.01	90.11	14.76	.50	1.02	.52

Notes. * p < .05; ** p < .01; *** p < .001. GDP = gross domestic product; SES = socioeconomic status.

Null Models

The ICCs revealed that a noteworthy portion of the variance in achievement in all three subjects was attributable to the country and school levels. Specifically, the ICCs demonstrated that 11.8% of the variance in mathematics achievement, 6.9% in reading achievement, and 7.9% in science achievement were at the school level. At the country level, the variances were even greater: 31.1% for mathematics, 29.1% for reading, and 29.5% for science. This means that a three-level analysis is appropriate to account for the variance attributable to the country and school levels.

Testing H1, H2, and H3

As shown in Table 3, we tested multilevel regression M1 models, and the results showed that mastery-approach goals were positively associated with mathematics (β = .09, p < .001), reading (β = .11, p < .001), and science (β = .09, p < .001) achievement. Thus, H1a, H1b, and H1c were supported.

Table 3.

Mastery-Approach Goals and Income Inequality as Predictors of Academic Achievement.

		Model 1			Model 2			Model 3
		M1a	M1b	M1c	M2a	M2b	M2c	M3a	M3b	M3c
	Achievement outcomes	Mathematics	Reading	Science	Mathematics	Reading	Science	Mathematics	Reading	Science
Predictors
Student Level
	Mastery-approach goals	.09 (.01)***	.11 (.01)***	.09 (.19)***	.09 (.01)***	.11 (.01)***	.09 (.01)***	.11 (.01)***	.09 (.01)***	.09 (.01)***
	SES	.21 (.01)***	.17 (.01)***	.19 (.01)***	.21 (.01)***	.17 (.01)***	.19 (.01)***	.20 (.01)***	.16 (.01)***	.18 (.01)***
	Grade							.01 (.03)	.00 (.02)	−.00 (.03)
	Gender							.08 (.01)***	−.12 (.01)***	.03 (.01)***
	Immigration status							−.07 (.02)***	−.08 (.01)***	−.08 (.01)***
School level
	School-level income inequality	−.29 (.04)***	−.29 (.04)***	−.29 (.04)***	−.29 (.04)***	−.29 (.04)***	−.29 (.04)***	−.29 (.04)***	−.29 (.04)***	−.29 (.04)***
Country level
	National-level income inequality	−.74 (.09)***	−.45 (.17)**	−.54 (.14)***	−.74 (.09)***	−.45 (.17)**	−.54 (.14)***	−.53 (.12)***	−.17 (.17)	−.29 (.16)
	GDP							.39 (.13)**	.56 (.17)**	.46 (.17)**
Cross-level Interaction
	Mastery-approach goals × school-level income inequality				−.03 (.00)***	−.03 (.00)***	−.03 (.00)***	−.03 (.00)***	−.03 (.00)***	−.03 (.00)***
	Mastery-approach goals × national-level income inequality				−.07 (.01)***	−.05 (.02)*	−.05 (.02)**	−.06 (.01)***	−.02 (.02)	−.03 (.02)
	School-level income inequality × SES				−.06 (.01)***	−.05 (.01)***	−.06 (.01)***	−.06 (.01)***	−.05 (.01)***	−.05 (.01)***
	National-level income inequality × SES				−.16 (.02)***	−.08 (.03)*	−.10 (.03)**	−.11 (.02)***	−.03 (.03)	−.05 (.03)
	Mastery-approach goals × school-level income inequality × SES				−.01 (.00)***	−.01 (.00)***	−.01 (.00)***	−.01 (.00)***	−.01 (.00)***	−.01 (.00)***
	Mastery-approach goals × national-level income inequality × SES				−.02 (.00)***	−.01 (.00)*	−.01 (.00)**	−.01 (.00)***	−.00 (.00)	−.01 (.00)

Notes. * p < .05; ** p < .01; *** p < .001. SES = socioeconomic status; GDP = gross domestic product.

Meanwhile, national-level income inequality was negatively associated with mathematics (β = −.74, p < .001), reading (β = −.45, p < .001), and science (β = −.54, p < .001) achievement. Hence, H2a, H2b, and H2c were supported. At the school level, the results indicated significant negative associations between school-level income inequality and achievement in mathematics (β = −.29, p < .001), reading (β = −.29, p < .001), and science (β = −.29, p < .001); thus, H3a, H3b, and H3c were supported.

Testing H4 and H5

The cross-level interaction between mastery-approach goals and national-level income inequality was negatively associated with achievement in mathematics (β = −.07, p < .001), reading (β = −.05, p < .001), and science (β = −.05, p < .01). Thus, H4a, H4b, and H4c were supported. Furthermore, the interaction between mastery-approach goals and school-level income inequality was negatively associated with achievement in mathematics (β = −.03, p < .001), reading (β = −.03, p < .001), and science (β = −.03, p < .001). Thus, H5a, H5b, and H5c were supported.

Testing H6 and H7

Mathematics (β = −.16, p < .001), reading (β = −.08, p < .05), and science (β = −.10, p < .01) were also negatively associated with the cross-level interaction between national-level income inequality and SES. Thus, H6a, H6b, and H6c were supported. Furthermore, the interaction between national-level income inequality and SES was significantly associated with mathematics (β = −.06, p < .001), reading (β = −.05, p < .001), and science achievement (β = −.06, p < .001). Thus, H7a, H7b, and H7c were supported.

Testing H8 and H9

Additionally, SES significantly moderated the interaction between mastery-approach goals and national-level income inequality on achievement in mathematics (β = −.02, p < .001), reading (β = −.01, p < .05), and science (β = −.01, p < .01). Thus, H8a, 8, and H8c were supported. Furthermore, the interaction among mastery-approach goals, school-level income inequality, and SES was significantly associated with mathematics (β = −.01, p < .001), reading (β = −.01, p < .001), and science achievement (β = −.01, p < .001). Thus, H9a, H9b, and H9c were supported.

Multilevel Moderation Models with Covariates

To enhance the robustness of the findings and account for potential confounding variables, we further tested the above multilevel moderation model with covariates. Most associations remained consistent after adjusting for these covariates; however, national-level income inequality was not significantly associated with reading (β = −.17, p = .31) and science achievement (β = −.29, p = .07). Meanwhile, all interactions involving national-level income inequality (mastery-approach goals × national-level income inequality, national-level income inequality × SES, and mastery-approach goals × national-level income inequality × SES) were no longer significantly associated with reading and science achievement, after covariates were controlled.

Discussion

The present study revealed three important findings. First, aligning with previous studies, we found positive associations between mastery-approach goals and academic achievement in mathematics, reading, and science.

Second, national- and school-level income inequality were negatively associated with achievement, but the strength of these associations varied across domains. Without controlling for GDP, national-level income inequality was associated with lower achievement across all three subjects, whereas with GDP included, only the negative association with mathematics achievement remained significant, consistent with previous research suggesting that mathematics may be especially sensitive to socioeconomic disparities (Condron, 2011; Dekker & Fischer, 2008). In contrast, school-level inequality was consistently and robustly associated with lower achievement in all three subjects.

Third, we found that both national- and school-level income inequality moderated the association between mastery-approach goals and academic achievement. These moderation effects were significant across three subjects before GDP adjustment, and the moderating roles of school-level income inequality were robust after GDP was included. At the national level, the weakening moderating role of income inequality on mastery-approach goals was more sensitive, disappearing for reading and science achievement once GDP was controlled.

Fourth, we extended prior research by showing that SES moderated these associations. Lower-SES students were disproportionately disadvantaged by national- and school-level income inequality, and the positive role of mastery-approach goals was further attenuated for lower-SES students in more unequal contexts.

Mastery-Approach Goals

We found that mastery-approach goals are directly associated with academic achievement in core subjects such as mathematics, reading, and science. These findings corroborate prior work which have consistently linked mastery-approach goals to favorable academic outcomes (Elliot & Sommet, 2023; Guo et al., 2023; Huang, 2012; Van Yperen et al., 2014). Furthermore, by investigating mastery-approach goals in the context of national and school-level income inequality, the present study extended prior work on achievement goals by highlighting the importance of a socio-ecological perspective (Wilkinson & Pickett, 2021).

Income Inequality

We found some differences between national- and school-level income inequality regarding academic achievement. The associations with national-level income inequality were more fragile to covariate adjustment. Our results revealed that significant negative associations with reading and science achievement disappeared after accounting for GDP, suggesting that broader economic prosperity and investment in human capital may partially offset the effects of inequality in these domains (Chiu, 2010; Hanushek & Woessmann, 2012). Mathematics achievement, however, remained linked to national-level income inequality, possibly because mathematics performance is more tightly tied to systemic inequalities in school funding, curricular quality, and teacher preparation (Condron, 2011; Dekker & Fischer, 2008).

School-level inequality, on the other hand, was consistently associated with lower achievement in mathematics, reading, and science. It is possible that the influence of school-level income inequality is experienced more proximally compared to national-level income inequality, which may exert a more distal influence. For instance, in highly unequal schools, learners might have unequal access to learning support or feel that academic success is less attainable (Blanden et al., 2023; Chiu, 2010). These issues may exert greater daily influence than broader, nationwide inequality that, while influential, can be less visible to students.

Robustness and Interpretation of GDP Effects

Importantly, the current findings revealed that some associations are highly stable across model specifications (e.g., school-level income inequality and SES), whereas others (e.g., national-level income inequality) are sensitive to the inclusion of GDP. This pattern indicates that inequality effects are not reducible to national wealth, but that the average level of economic development interacts with inequality in students’ academic achievement. One possible explanation is that GDP captures broader investments in educational infrastructure and social protection (Hanushek & Woessmann, 2012; OECD, 2019), which can buffer the negative impact of inequality in domains such as reading and science, where exposure to cultural resources and language-rich environments plays a central role (Evans et al., 2010). Mathematics, in contrast, may be more directly dependent on the quality of schooling and instructional practices, which remain highly stratified in unequal contexts, even after accounting for national prosperity.

Moderating Roles of National- and School-Level Income Inequality

A key novelty of our research lies in its examination of how mastery-approach goals function within varying levels of income inequality at the national and school levels. In environments with lower income inequality, mastery-approach goals are effectively associated with academic achievement because these settings offer more equitable access to educational resources and opportunities (Chiu, 2010; King et al., 2022). In contrast, in higher inequality settings, the effectiveness of mastery-approach goals may be compromised. Students in these environments face structural barriers that can impede their ability to engage fully with learning despite their achievement goals. Linking achievement goals to socio-ecological factors such as income inequality offers a more comprehensive understanding of how personal and contextual factors interact in influencing educational outcomes.

Moreover, our research contributes significantly to the broader discourse on income inequality and education by demonstrating that these economic disparities can attenuate the association between mastery-approach goals and academic achievement. Previous studies have predominantly focused on more immediate or direct impacts of inequality on education outcomes (Du et al., 2024; King et al., 2022). Our findings suggest that the associations between achievement goals and academic achievement are also crucially shaped by the broader socioeconomic contexts.

Furthermore, SES compounded these unequal dynamics. Lower-SES students were doubly disadvantaged, as income inequality both directly lowered their academic achievement and reduced the positive returns of mastery-approach goals. These findings align with previous research showing that inequality operates through both structural and psychological mechanisms, which disproportionately undermine the performance of lower-SES students (Claes et al., 2024; King et al., 2022).

Theoretical and Practical Implications

This study significantly contributes to the integration of achievement goal theory with the socio-ecological perspective, research areas that have traditionally progressed independently. While extensive research has explored achievement goals within educational psychology, less attention has been paid to how socio-ecological contexts, such as national-level and school-level income inequality, impact these goals and the subsequent academic outcomes.

Theoretically, these findings broaden the scope of achievement goal theory by integrating it with perspectives on income inequality. Achievement goal theory emphasizes that competence—whether it centers on personal growth or outperforming others—shapes students’ motivation and engagement (Elliot & Sommet, 2023). Our results indicate that mastery-approach goals might not function uniformly across all contexts. In higher-inequality environments, students may be more aware of disparities in resources and support, which can undermine their motivation to strive for improvement. Mastery-approach goals, while generally linked to deeper learning and resilience, may be undercut by external obstacles such as insufficient learning materials or peers who face similar resource-related challenges.

By linking mastery-approach goals with income inequality, our research illustrates and highlights how a socio-ecological perspective reveals the interplay between achievement goals and the broader socio-ecological context. This idea resonates with socio-ecological views of learning, where individual motivation is influenced by nested contexts (Skinner et al., 2022). From a theoretical perspective, it is likely that the observed associations are not unique to mastery-approach goals. Although this study was limited to mastery-approach goals, performance-approach and performance-avoidance goals have also been found to be significantly influenced by competitive environments (Elliot et al., 2018; Van Yperen et al., 2014). It is possible that income inequality could also shape performance-based goals by intensifying social comparison and fostering perceptions of relative deprivation (Rodriguez-Bailon et al., 2017; Sommet & Elliot, 2023). Inequality might also influence performance-based goals by amplifying competition and heightening anxiety. These ideas remain speculative, and future research is needed to determine whether such mechanisms operate consistently across different achievement goals.

Practically, the present research underscores the need for policymakers and practitioners to make learning settings more equitable so that mastery-approach goal pursuit can thrive. This could involve distributing resources more evenly, offering strong support to students from underprivileged backgrounds, and creating targeted programs that address diverse socioeconomic conditions (Muijs et al., 2004; Reimers, 2000).

In addition, these findings highlight how socio-ecological contexts can reduce the effectiveness of mastery-approach goals in certain schools. While mastery-approach goals generally support academic success, they may not be as helpful when there is a large income gap among students. Consequently, interventions aimed at strengthening mastery-approach goals should also address the effects of income inequality to ensure that all students can benefit.

Limitations and Future Directions

Several limitations of this research should be acknowledged. First, the use of PISA data, which are cross-sectional, does not allow a test of the causal relationships among mastery-approach goals, income inequality, and academic achievement. Longitudinal studies and experimental designs are necessary to determine causal relationships and track the development of achievement goals and income inequality over time.

Second, our analysis was restricted to 15-year-old students participating in the PISA. The associations among mastery-approach goals, income inequality, and academic achievement might differ across various age groups. Future research could benefit from examining these associations among different age groups.

Third, this study's focus was solely on mastery-approach goals, as these were the only achievement goals measured in the PISA 2018 dataset. This restriction precluded examination of other achievement goals, namely performance-approach, performance-avoidance, and mastery-avoidance goals. Future research would do well to include a broader range of achievement goals to develop a more comprehensive understanding of how inequality might moderate the influence of achievement goals.

Fourth, we focused on national and school-level income inequality but not other aspects of inequality, such as racial inequality and neighborhood segregation. Previous studies indicate that racial and neighborhood inequities can impact students’ everyday experiences and social interactions (Gordils et al., 2020; Pabayo et al., 2014; Roksa et al., 2017), potentially influencing their motivation and academic trajectory. Future research could incorporate these additional demographic and structural dimensions to provide a more comprehensive understanding of how various socio-ecological factors intersect with achievement goals to influence educational outcomes.

Conclusion

The present research advances our understanding of how mastery-approach goals relate to academic achievement within broader socio-ecological contexts. While mastery-approach goals are positively associated with achievement, their benefits are weakened in more unequal schools and countries, particularly for lower-SES students. Overall, our study highlights the need to address both achievement goals and broader socio-ecological contexts to foster academic success for all students.

Footnotes

ORCID iDs

Yikang Chen

Ronnel B. King

Funding

This research was fundedby the Hong Kong General Research Fund (RGC Ref No. 14612424) and The Chinese University of Hong Kong Direct Grant (Project Code: 4058101) conferred to the corresponding author.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Notes

References

Baranik

L. E.

Stanley

L. J.

Bynum

B. H.

Lance

C. E.

(2010). Examining the construct validity of mastery-avoidance achievement goals: A meta-analysis. Human Performance, 23(3), 265–282. https://doi.org/10.1080/08959285.2010.488463

Belenky

D. M.

Nokes-Malach

T. J.

(2012). Motivation and transfer: The role of mastery-approach goals in preparation for future learning. Journal of the Learning Sciences, 21(3), 399–432. https://doi.org/10.1080/10508406.2011.651232

Belenky

D. M.

Nokes-Malach

T. J.

(2013). Mastery-approach goals and knowledge transfer: An investigation into the effects of task structure and framing instructions. Learning and Individual Differences, 25, 21–34. https://doi.org/10.1016/j.lindif.2013.02.004

Berkowitz

Moore

Astor

R. A.

Benbenishty

(2017). A research synthesis of the associations between socioeconomic background, inequality, school climate, and academic achievement. Review of Educational Research, 87(2), 425–469. https://doi.org/10.3102/0034654316669821

Blanden

Doepke

Stuhler

(2023). Educational inequality. In Handbook of the economics of education (vol. 6, pp. 405–497). Elsevier. https://doi.org/10.1016/bs.hesedu.2022.11.003

Blascovich

(2013). The biopsychosocial model of challenge and threat: Reflections, theoretical ubiquity, and new directions. In Neuroscience of prejudice and intergroup relations (pp. 229–242). Psychology Press.

Brian

(2015). OECD insights income inequality the gap between rich and poor: The gap between rich and poor. OECD Publishing.

Broer

Bai

Fonseca

Broer

Bai

Fonseca

(2019). A review of the literature on socioeconomic status and educational achievement. In Socioeconomic inequality and educational outcomes. IEA research for education (Vol. 5). Springer. https://doi.org/10.1007/978-3-030-11991-1_2

Browman

A. S.

Destin

Kearney

M. S.

Levine

P. B.

(2019). How economic inequality shapes mobility expectations and behaviour in disadvantaged youth. Nature Human Behaviour, 3(3), 214–220. https://doi.org/10.1038/s41562-018-0523-0

10.

Burnette

J. L.

O'boyle

E. H.

VanEpps

E. M.

Pollack

J. M.

Finkel

E. J.

(2013). Mind-sets matter: A meta-analytic review of implicit theories and self-regulation. Psychological Bulletin, 139(3), 655. https://doi.org/10.1037/a0029531

11.

Canning

E. A.

LaCosse

Kroeper

K. M.

Murphy

M. C.

(2020). Feeling like an imposter: The effect of perceived classroom competition on the daily psychological experiences of first-generation college students. Social Psychological and Personality Science, 11(5), 647–657. https://doi.org/10.1177/1948550619882032

12.

Cellar

D. F.

Stuhlmacher

A. F.

Young

S. K.

Fisher

D. M.

Adair

C. K.

Haynes

Twichell

Arnold

K. A.

Royer

Denning

B. L.

Riester

(2011). Trait goal orientation, self-regulation, and performance: A meta-analysis. Journal of Business and Psychology, 26(4), 467–483. https://doi.org/10.1007/s10869-010-9201-6

13.

Chiu

M. M.

(2010). Effects of inequality, family and school on mathematics achievement: Country and student differences. Social Forces, 88(4), 1645–1676. https://doi.org/10.1353/sof.2010.0019

14.

Chiu

M. M.

(2015). Family inequality, school inequalities, and mathematics achievement in 65 countries: Microeconomic mechanisms of rent seeking and diminishing marginal returns. Teachers College Record, 117(1), 1–32. https://doi.org/10.1177/016146811511700105

15.

Chiu

M. M.

(2017). Family and school inequality reduce education resources and overall student learning. Advances in Sociology Research, 24, 61–98.

16.

Chiu

M. M.

Chow

B. W.-Y.

(2015). Classmate characteristics and student achievement in 33 countries: Classmates’ past achievement, family socioeconomic status, educational resources, and attitudes toward reading. Journal of Educational Psychology, 107(1), 152. https://doi.org/10.1037/a0036897

17.

Chiu

M. M.

McBride-Chang

(2010). Family and reading in 41 countries: Differences across cultures and students. Scientific Studies of Reading, 14(6), 514–543. https://doi.org/10.1080/10888431003623520

18.

Claes

Smeding

Carré

Sommet

(2024). The social class test gap: A worldwide investigation of the role of academic anxiety and income inequality in standardized test score disparities. Journal of Educational Psychology, 116(6), 871–888. https://doi.org/10.1037/edu0000881

19.

Condron

D. J.

(2011). Egalitarianism and educational excellence: Compatible goals for affluent societies? Educational Researcher, 40(2), 47–55. https://doi.org/10.3102/0013189X11401021

20.

Corak

(2013). Income inequality, equality of opportunity, and intergenerational mobility. Journal of Economic Perspectives, 27(3), 79–102. https://doi.org/10.1257/jep.27.3.79

21.

Dekker

Fischer

(2008). Cultural differences in academic motivation goals: A meta-analysis across 13 societies. The Journal of Educational Research, 102(2), 99–110. https://doi.org/10.3200/JOER.102.2.99-110

22.

Doll

(2022). Integrating assessment into school environments to promote Students’ psychological well-being. In Handbook of positive psychology in schools (pp. 230–245). Routledge.

23.

(2024). Economic inequality. In Maddux

J. E.

(Ed.), Subjective well-being and life satisfaction (pp. 345–362). Routledge.

24.

Götz

F. M.

King

R. B.

Rentfrow

P. J.

(2024). The psychological imprint of inequality: Economic inequality shapes achievement and power values in human life. Journal of Personality, 92(1), 222–242. https://doi.org/10.1111/jopy.12758

25.

Due

Merlo

Harel-Fisch

Damsgaard

M. T.

soc

M. s.

Holstein

B. E.

soc

M. s.

Hetland

Currie

Gabhainn

S. N.

(2009). Socioeconomic inequality in exposure to bullying during adolescence: A comparative, cross-sectional, multilevel study in 35 countries. American Journal of Public Health, 99(5), 907–914. https://doi.org/10.2105/AJPH.2008.139303

26.

Dweck

C. S.

(1986). Motivational processes affecting learning. American Psychologist, 41(10), 1040. https://doi.org/10.1037/0003-066X.41.10.1040

27.

Dweck

C. S.

Leggett

E. L.

(1988). A social-cognitive approach to motivation and personality. Psychological Review, 95(2), 256. https://doi.org/10.1037/0033-295X.95.2.256

28.

Elliot

(1997). Integrating the “classic” and “contemporary” approaches to achievement motivation: A hierarchical model of approach and avoidance achievement motivation. Advanced in Motivation and Achievement, 10(7), 143–179.

29.

Elliot

A. J.

(1999). Approach and avoidance motivation and achievement goals. Educational Psychologist, 34(3), 169–189. https://doi.org/10.1207/s15326985ep3403_3

30.

Elliot

A. J.

Harackiewicz

J. M.

(1996). Approach and avoidance achievement goals and intrinsic motivation: A mediational analysis. Journal of Personality and Social Psychology, 70(3), 461. https://doi.org/10.1037/0022-3514.70.3.461

31.

Elliot

A. J.

Hulleman

C. S.

(2017). Achievement goals. In A. J. Elliot, C. S. Dweck, & D. S. Yeager (Eds.), Handbook of competence and motivation: Theory and application (Vol. 2, pp. 43–60). The Guilford Press.

32.

Elliot

A. J.

Jury

Murayama

(2018). Trait and perceived environmental competitiveness in achievement situations. Journal of Personality, 86(3), 353–367. https://doi.org/10.1111/jopy.12320

33.

Elliot

A. J.

McGregor

H. A.

(2001). A 2× 2 achievement goal framework. Journal of Personality and Social Psychology, 80(3), 501. https://doi.org/10.1037/0022-3514.80.3.501

34.

Elliot

A. J.

Murayama

(2008). On the measurement of achievement goals: Critique, illustration, and application. Journal of Educational Psychology, 100(3), 613–628. https://doi.org/10.1037/0022-0663.100.3.613

35.

Elliot, A. J., Murayama, K., & Pekrun, R. (2011). A 3 × 2 achievement goal model. Journal of Educational Psychology, 103(3), 632–648. https://doi.org/10.1007/s10648-023-09785-7

36.

Elliot

A. J.

Sommet

(2023). Integration in the achievement motivation literature and the hierarchical model of achievement motivation. Educational Psychology Review, 35(3), 77. https://doi.org/10.1007/s10648-023-09785-7

37.

Elliot

A. J.

Thrash

T. M.

(2001). Achievement goals and the hierarchical model of achievement motivation. Educational Psychology Review, 13(2), 139–156. https://doi.org/10.1023/A:1009057102306

38.

Enders

C. K.

Tofighi

(2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12(2), 121. https://doi.org/10.1037/1082-989X.12.2.121

39.

Erentaitė

Vosylis

(2025). Perceived goal structures and student outcomes: Consistency of motivational experiences within and across classrooms. Learning and Individual Differences, 117, 102594. https://doi.org/10.1016/j.lindif.2024.102594

40.

Erentaitė

Vosylis

Raižienė

Simonaitienė

Augustinienė

Branje

(2023). Profiles of achievement goal orientations among adolescents with different SES background before and during the COVID-19 pandemic. European Journal of Developmental Psychology, 20(4), 597–615. https://doi.org/10.1080/17405629.2022.2122435

41.

Evans

M. D.

Kelley

Sikora

Treiman

D. J.

(2010). Family scholarly culture and educational success: Books and schooling in 27 nations. Research in Social Stratification and Mobility, 28(2), 171–197. https://doi.org/10.1016/j.rssm.2010.01.002

42.

García-Sánchez

García-Castro

J. D.

Willis

G. B.

Rodríguez-Bailón

(2024). Economic inequality and unfairness evaluations of income distribution negatively predict political and social trust: Evidence from Latin America over 23 years. Social Psychological and Personality Science, 16(4), 347–358. https://doi.org/10.1177/19485506231221310

43.

Gordils

Sommet

Elliot

A. J.

Jamieson

J. P.

(2020). Racial income inequality, perceptions of competition, and negative interracial outcomes. Social Psychological and Personality Science, 11(1), 74–87. https://doi.org/10.1177/1948550619837003

44.

Guo

Elliot

A. J.

Marsh

H. W.

Murayama

Basarkod

Parker

P. D.

Dicke

(2023). Mastery-approach goals: A large-scale cross-cultural analysis of antecedents and consequences. Journal of Personality and Social Psychology, 125(2), 397. https://doi.org/10.1037/pspp0000436

45.

Hanushek

E. A.

Woessmann

(2012). Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation. Journal of Economic Growth, 17(4), 267–321. https://doi.org/10.1007/s10887-012-9081-x

46.

Huang

(2011). Achievement goals and achievement emotions: A meta-analysis. Educational Psychology Review, 23(3), 359–388. https://doi.org/10.1007/s10648-011-9155-x

47.

Huang

(2012). Discriminant and criterion-related validity of achievement goals in predicting academic achievement: A meta-analysis. Journal of Educational Psychology, 104(1), 48. https://doi.org/10.1037/a0026223

48.

Huang

(2016). Achievement goals and self-efficacy: A meta-analysis. Educational Research Review, 19, 119–137. https://doi.org/10.1016/j.edurev.2016.07.002

49.

Hulleman

C. S.

Schrager

S. M.

Bodmann

S. M.

Harackiewicz

J. M.

(2010). A meta-analytic review of achievement goal measures: Different labels for the same constructs or different constructs with similar labels? Psychological Bulletin, 136(3), 422. https://doi.org/10.1037/a0018947

50.

Jamieson

J. P.

Elliot

A. J.

(2018). To approach or to avoid: Integrating the biopsychosocial model of challenge and threat with theories from affective dynamics and motivation science.

51.

Katz-Vago

Benita

(2024). Mastery-approach and performance-approach goals predict distinct outcomes during personal academic goal pursuit. British Journal of Educational Psychology, 94(2), 309–327. https://doi.org/10.1111/bjep.12645

52.

King

R. B.

(2022). Sociocultural and ecological perspectives on achievement motivation. Asian Journal of Social Psychology, 25(3), 433–448. https://doi.org/10.1111/ajsp.12507

53.

King

R. B.

Cai

Elliot

A. J.

(2024a). Income inequality is associated with heightened test anxiety and lower academic achievement: A cross-national study in 51 countries. Learning and Instruction, 89, 101825. https://doi.org/10.1016/j.learninstruc.2023.101825

54.

King

R. B.

Chiu

M. M.

(2022). Greater income inequality, lower school belonging: Multilevel and cross-temporal analyses of 65 countries. Journal of Educational Psychology, 114(5), 1101. https://doi.org/10.1037/edu0000736

55.

King

R. B.

Wang

Leung

S. O.

Elliot

(2024b). Socio-economic status, mastery-approach goals and learning-related outcomes: Mediation and moderation. British Journal of Educational Psychology, 94(2), 499–517. https://doi.org/10.1111/bjep.12660

56.

Layte

(2012). The association between income inequality and mental health: Testing status anxiety, social capital, and neo-materialist explanations. European Sociological Review, 28(4), 498–511. https://doi.org/10.1093/esr/jcr012

57.

Lechner

C. M.

Bhaktha

Groskurth

Bluemke

(2021). Why ability point estimates can be pointless: A primer on using skill measures from large-scale assessments in secondary analyses. Measurement Instruments for the Social Sciences, 3(2), 1–16. https://doi.org/10.1186/s42409-020-00020-5

58.

Levin

H. M.

(2013). The utility and need for incorporating noncognitive skills into large-scale educational assessments. In von Davier

Gonzalez

Kirsch

Yamamoto

(Eds.), The role of international large-scale assessments: Perspectives from technology, economy, and educational research (pp. 67–86). Springer Netherlands. https://doi.org/10.1007/978-94-007-4629-9_5

59.

Liang

King

R. B.

Sing

C. C.

(2024). Mapping the terrain of students well-being: Implications for academic achievement. Child Indicators Research, 18, 57–92. https://doi.org/10.1007/s12187-024-10201-x

60.

Liem

G. A. D.

Elliot

A. J.

(2018). Sociocultural influences on achievement goal adoption and regulation. Information Age Publishing.

61.

Marjoram, P., Molitor, J., Plagnol, V., & Tavaré, S. (2003). Markov chain Monte Carlo without likelihoods. Proceedings of the National Academy of Sciences, 100(26), 15324–15328. https://doi.org/10.1073/pnas.0306899100

62.

Marks

G. N.

(2006). Are between-and within-school differences in student performance largely due to socio-economic background? Evidence from 30 countries. Educational Research, 48(1), 21–40. https://doi.org/10.1080/00131880500498396

63.

Marks

G. N.

Cresswell

Ainley

(2006). Explaining socioeconomic inequalities in student achievement: The role of home and school factors. Educational Research and Evaluation, 12(02), 105–128. https://doi.org/10.1080/13803610600587040

64.

Menzer

M. M.

Torney-Purta

(2012). Individualism and socioeconomic diversity at school as related to perceptions of the frequency of peer aggression in fifteen countries. Journal of Adolescence, 35(5), 1285–1294. https://doi.org/10.1016/j.adolescence.2012.04.013

65.

Michael

Kyriakides

(2023). Mediating effects of motivation and socioeconomic status on reading achievement: A secondary analysis of PISA 2018. Large-scale Assessments in Education, 11(1), 31. https://doi.org/10.1186/s40536-023-00181-9

66.

Mouratidis

Michou

Demircioğlu

A. N.

Sayil

(2018). Different goals, different pathways to success: Performance-approach goals as direct and mastery-approach goals as indirect predictors of grades in mathematics. Learning and Individual Differences, 61, 127–135. https://doi.org/10.1016/j.lindif.2017.11.017

67.

Muijs

Harris

Chapman

Stoll

Russ

(2004). Improving schools in socioeconomically disadvantaged areas–A review of research evidence. School Effectiveness and School Improvement, 15(2), 149–175. https://doi.org/10.1076/sesi.15.2.149.30433

68.

Nicholls

J. G.

(1984). Achievement motivation: Conceptions of ability, subjective experience, task choice, and performance. Psychological Review, 91(3), 328. https://doi.org/10.1037/0033-295X.91.3.328

69.

OECD . (2009). PISA data analysis manual: SPSS, second edition. OECD Publishing. https://www.oecd-ilibrary.org/education/pisa-data-analysis-manual-spss-secondedition_9789264056275-en .

70.

OECD. (2019a). Assessment and analytical framework. Organisation for Economic Cooperation and Development Publishing. https://doi.org/10.1787/b25efab8-en

71.

OECD. (2019b). PISA 2018 Results (Vol. I): What students know and can do. OECD Publishing. https://doi.org/10.1787/5f07c754-en

72.

Oishi

Choi

(2017). Culture and motivation: A socio-ecological approach. In Advances in motivation science (Vol. 4, pp. 141–170). Elsevier. https://doi.org/10.1016/bs.adms.2017.02.004

73.

Owens

(2018). Income segregation between school districts and inequality in students’ achievement. Sociology of Education, 91(1), 1–27. https://doi.org/10.1177/0038040717741180

74.

Owens

Reardon

S. F.

Jencks

(2016). Income segregation between schools and school districts. American Educational Research Journal, 53(4), 1159–1197. https://doi.org/10.3102/000283121665272

75.

Oxfam. (2024). Inequality inc.: How corporate power divides our world and the need for a new era of public action. Oxfam GB Oxford.

76.

Pabayo

Molnar

B. E.

Kawachi

(2014). The role of neighborhood income inequality in adolescent aggression and violence. Journal of Adolescent Health, 55(4), 571–579. https://doi.org/10.1016/j.jadohealth.2014.04.012

77.

Patrick

Kaplan

Ryan

A. M.

(2011). Positive classroom motivational environments: Convergence between mastery goal structure and classroom social climate. Journal of Educational Psychology, 103(2), 367. https://doi.org/10.1037/a0023311

78.

Payne

S. C.

Youngcourt

S. S.

Beaubien

J. M.

(2007). A meta-analytic examination of the goal orientation nomological net. Journal of Applied Psychology, 92(1), 128. https://doi.org/10.1037/0021-9010.92.1.128

79.

Pekrun

Elliot

A. J.

Maier

M. A.

(2009). Achievement goals and achievement emotions: Testing a model of their joint relations with academic performance. Journal of Educational Psychology, 101(1), 115. https://doi.org/10.1037/a0013383

80.

Pintrich

P. R.

(2000). Multiple goals, multiple pathways: The role of goal orientation in learning and achievement. Journal of Educational Psychology, 92(3), 544. https://doi.org/10.1037/0022-0663.92.3.544

81.

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Sage.

82.

Reardon, S. F., & Bischoff, K. (2011). Income Inequality and Income Segregation. American Journal of Sociology, 116(4), 1092–1153. https://doi.org/10.1086/657114

83.

Reimers

(2000). Unequal schools, unequal chances: The challenges to equal opportunity in the Americas (Vol. 5). Harvard University Press, MA.

84.

Rodriguez-Bailon

Bratanova

Willis

G. B.

Lopez-Rodriguez

Sturrock

Loughnan

(2017). Social class and ideologies of inequality: How they uphold unequal societies. Journal of Social Issues, 73(1), 99–116. https://doi.org/10.1111/josi.12206

85.

Roksa

Trolian

T. L.

Pascarella

E. T.

Kilgo

C. A.

Blaich

Wise

K. S.

(2017). Racial inequality in critical thinking skills: The role of academic and diversity experiences. Research in Higher Education, 58(2), 119–140. https://doi.org/10.1007/s11162-016-9423-1

86.

Sánchez-Rodríguez

Willis

G. B.

Jetten

Rodríguez-Bailón

(2019). Economic inequality enhances inferences that the normative climate is individualistic and competitive. European Journal of Social Psychology, 49(6), 1114–1127. https://doi.org/10.1002/ejsp.2557

87.

Schneider, D., Hastings, O. P., & LaBriola, J. (2018). Income inequality and class divides in parental investments. American Sociological Review, 83(3), 475–507. https://doi.org/10.1177/0003122418772034

88.

Seery

M. D.

(2013). The biopsychosocial model of challenge and threat: Using the heart to measure the mind. Social and Personality Psychology Compass, 7(9), 637–653. https://doi.org/10.1111/spc3.12052

89.

Skinner

E. A.

Kindermann

T. A.

Vollet

J. W.

Rickert

N. P.

(2022). Complex social ecologies and the development of academic motivation. Educational Psychology Review, 34(4), 2129–2165. https://doi.org/10.1007/s10648-022-09714-0

90.

Solt

(2016). The standardized world income inequality database. Social Science Quarterly, 97(5), 1267–1281. https://doi.org/10.1111/ssqu.12295

91.

Solt

(2020). Measuring income inequality across countries and over time: The standardized world income inequality database. Social Science Quarterly, 101(3), 1183–1199. https://doi.org/10.1111/ssqu.12795

92.

Sommet

Claes

Elliot

A. J.

(2024). Economic inequality and student outcomes: What we know and where to go from here. Motivation Science, 10(7), 247–261. https://doi.org/10.1037/mot0000335

93.

Sommet

Elliot

A. J.

(2023). A competitiveness-based theoretical framework on the psychology of income inequality. Current Directions in Psychological Science, 32(4), 318–327. https://doi.org/10.1177/09637214231159563

94.

Sommet

Weissman

D. L.

Elliot

A. J.

(2023). Income inequality predicts competitiveness and cooperativeness at school. Journal of Educational Psychology, 115(1), 173. https://doi.org/10.1037/edu0000731

95.

Thorson

G. R.

Gearhart

S. M.

(2018). The adverse effects of economic inequality on educational outcomes: An examination of PISA scores, 2000–2015. World Affairs, 181(3), 286–306. https://doi.org/10.1177/0043820018799425

96.

Topuz

S. G.

(2022). The relationship between income inequality and economic growth: Are transmission channels effective? Social Indicators Research, 162(3), 1177–1231. https://doi.org/10.1007/s11205-022-02882-0

97.

Urdan

Kaplan

(2020). The origins, evolution, and future directions of achievement goal theory. Contemporary Educational Psychology, 61, 101862. https://doi.org/10.1016/j.cedpsych.2020.101862

98.

Van Yperen

N. W.

Blaga

Postmes

(2014). A meta-analysis of self-reported achievement goals and nonself-report performance across three achievement domains (work, sports, and education). PLoS One, 9(4), e93594. https://doi.org/10.1371/journal.pone.0093594

99.

Wang

(2023). Income inequality, cultural capital, and high school students’ academic achievement in OECD countries: A moderated mediation analysis. The British Journal of Sociology, 74(2), 148–172. https://doi.org/10.1111/1468-4446.12997

100.

Wilkinson

Pickett

(2021). For better or worse? In The struggle for social sustainability (pp. 275–290). Policy Press. https://doi.org/10.51952/9781447356127.ch014

101.

Wilkinson

R. G.

Pickett

K. E.

(2009). Income inequality and social dysfunction. Annual Review of S Ociology, 35(1), 493–511. https://doi.org/10.1146/annurev-soc-070308-115926

102.

Willis

G. B.

García-Sánchez

Sánchez-Rodríguez

García-Castro

J. D.

Rodríguez-Bailón

(2022). The psychosocial effects of economic inequality depend on its perception. Nature Reviews Psychology, 1(5), 301–309. https://doi.org/10.1038/s44159-022-00044-0

103.

Wirthwein

Sparfeldt

J. R.

Pinquart

Wegerer

Steinmayr

(2013). Achievement goals and academic achievement: A closer look at moderating factors. Educational Research Review, 10, 66–89. https://doi.org/10.1016/j.edurev.2013.07.001

104.

Workman

(2023). Income inequality and student achievement: Trends among US states (1992–2019). Educational Review, 75(5), 871–893. https://doi.org/10.1080/00131911.2021.1974349

105.

World Bank. (2019). World bank open data. https://data.worldbank.org/

106.

Yan

Chiu

M. M.

(2023). The relationship between formative assessment and reading achievement: A multilevel analysis of students in 19 countries/regions. British Educational Research Journal, 49(1), 186–208. https://doi.org/10.1002/berj.3837

107.

Zusho

Clayton

(2011). Culturalizing achievement goal theory and research. Educational Psychologist, 46(4), 239–260. https://doi.org/10.1080/00461520.2011.614526