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To What Extent and Under Which Circumstances Are Growth Mind-Sets Important to Academic Achievement? Two Meta-Analyses
Abstract
Mind-sets (aka implicit theories) are beliefs about the nature of human attributes (e.g., intelligence). The theory holds that individuals with growth mind-sets (beliefs that attributes are malleable with effort) enjoy many positive outcomes—including higher academic achievement—while their peers who have fixed mind-sets experience negative outcomes. Given this relationship, interventions designed to increase students’ growth mind-sets—thereby increasing their academic achievement—have been implemented in schools around the world. In our first meta-analysis (k = 273, N = 365,915), we examined the strength of the relationship between mind-set and academic achievement and potential moderating factors. In our second meta-analysis (k = 43, N = 57,155), we examined the effectiveness of mind-set interventions on academic achievement and potential moderating factors. Overall effects were weak for both meta-analyses. However, some results supported specific tenets of the theory, namely, that students with low socioeconomic status or who are academically at risk might benefit from mind-set interventions.
... So, is growth mindset beneficial for students' statistics learning? We suggest that the relationship between growth mindset and statistics achievement may be more complicated than previously thought (Dweck & Yeager, 2019;Hong et al., 2020;Sisk et al., 2018;Yeager & Dweck, 2020). ...
... In recent years, learning sciences has become an international topic of great interest (Zeng, 2015). A growing body of academic research examines the effect of one's growth mindset on learning outcomes (Bahník & Vranka, 2017;Blackwell et al., 2007;Li & Bates, 2020;Rattan et al., 2015;Sisk et al., 2018) So, how does growth mindset contribute to academic achievement? ...
... However, with the prevalence of research into growth mindsets, a growing body of research suggests that the relationship between growth mindsets and academic achievement (i.e., grades) is not as consistent as expected (Bahník & Vranka, 2017;Li & Bates, 2020). A meta-analysis of 129 empirical studies examine the relationship between mindsets and academic achievement (Sisk et al., 2018) and it was found that only 37 % of the studies had a positive correlation and 58 % did not find a significant correlation, thus challenging and questioning the effect that growth mindset has on learning outcomes (Bahník & Vranka, 2017;Li & Bates, 2020). With this in mind, does growth mindset have a positive effect on learning outcomes (academic achievement) and, how does it do so? ...
The purpose of this study is to investigate whether "growth mindset" contributes to statistical learning in the social sciences, and to address the controversy whether growth mindset is positively associated with academic achievement. Students enrolled in a statistics course were invited to participate in a two-stage online questionnaire , resulting in cross-sectional data (including only the first-wave data) and longitudinal data (two-wave data). Both cross-sectional and longitudinal data show that compared with general growth mindset, statistical growth mindset has a significant indirect effect on academic achievement in statistics courses acting through academic engagement, which supports the idea of "domain specificity". Perceived difficulty moderated the effect of the statistical growth mindset and academic engagement, only in cross-sectional data. This study provides empirical evidence for the application of a statistical growth mindset to statistical learning and partially addresses the controversy over the predictive effect of growth mindset. Educational relevance statement Statistics is an essential knowledge and fundamental skill in the studies of social sciences. It is also a mandatory course for most undergraduate students majoring in social sciences. However, many students encounter difficulties during the process of learning statistics, which leads to less than ideal academic performance. This study is grounded in the theory of growth mindset and aims to explore how growth mindset of statistical abilities (statistical growth mindset) can contribute to statistics learning. Through both cross-sectional and longitudinal data analysis, it was found that in comparison to general growth mindset related to intelligence, statistical growth mindset has a better ability to predict academic engagement and subsequently better statistics grades. Furthermore, the analysis of cross-sectional data revealed that the perceived difficulty of the statistics course moderates the relationship between statistical growth mindset and academic engagement. Specifically , for students who perceive the course as more difficult and challenging , statistical growth mindset exhibits greater predictive power on academic engagement. The increased engagement, in turn, is positively associated with statistics grades. Therefore, it is evident that statistical growth mindset, rather than a general growth mindset, is more likely to play a role in facilitating statistics learning, particularly for students who find statistics more challenging. The paper also includes a discussion on the potential principles and directions for designing interventions aimed at promoting students' statistical growth mindset, offering insights for future instructional interventions to enhance statistics learning.
... Researchers have investigated mindsets because the constructs play a vital role in explaining individuals' motivation, actions, or outcomes in education and psychology. Along with this research stream, two recent studies (Burnette et al., 2013;Sisk et al., 2018) that synthesized previous research on mindsets found positive relationships with outcomes. One of the meta-analyses on mindsets in general conducted by Burnette et al. (2013) showed that incremental mindsets had a weak, but positive correlation with achievements (r = .095, ...
... p < .01). The other study conducted by Sisk et al. (2018) also focused on the effects of growth mindsets on academic achievements. Similar results to Burnette et al. were obtained, which showed that growth mindsets had weak, but positive correlations with academic achievements (r = .10, ...
... This result is consistent with Lou and Noels (2017). We referred to scores of subjective language proficiency as a measure of predictive validity, but other studies (e.g., Sisk et al., 2018) have examined the relationship with objective outcomes such as academic scores. These have indicated contradictory results to what was revealed in this study (weak but positive effects of mindsets on perceived achievements) such as course exams (r = .08, ...
Researchers from various academic fields have paid close attention to mindsets for many years, and second language acquisition is no exception. Previous studies in SLA introduced language-specific mindsets by developing the Language Mindsets Inventory scale to examine language learners' mindsets. Although a Japanese version of this scale has already been developed, there may be room for improvement in terms of translation and validation. The current study aimed to develop a more refined Japanese version of the scale through a careful translation process and a thorough examination of its reliability and validity. Participants in the current study were 179 EFL university students in Japan. Results of the reliability analysis showed that the new Japanese-translated scale with 12 items was highly reliable. Confirmatory factor analysis also showed that the four-factor model had a good fit and further evidence was added by examining the convergent, discriminant, and concurrent validity. The outcomes of the study imply that mindsets may not transcend subject areas which suggest the development of skill-specific mindset scales.
... (Yeager & Dweck, 2020, p. 1277 We find ourselves championing a third perspective, agreeing with Macnamara and Burgoyne (2023) on some issues and with growth mindset proponents on others. But taken together, we argue that the current body of growth mindset research achieves neither the predictive ability of a rigorous theory (the theory is underspecified) nor the desired outcomes of a purely pragmatic "broad-spectrum" approach (the effect sizes are small; Macnamara & Burgoyne, 2023;Sisk et al., 2018). In this commentary, we first address common theoretical issues pertaining to growth mindset theory and interventions, we then address the pragmatic issues with the expected (heterogeneity of) effects of growth mindset interventions. ...
... Although Macnamara and Burgoyne (2023) mention Limeri et al.'s (2020) work in their discussion, the critical importance of these findings cannot be understated. Consider that growth mindset correlates with academic performance at approximately r = 0.10 (Sisk et al., 2018). Given Limeri et al.'s (2020) findings, this correlation could be contaminated with whichever demographic, academic, or other factors correlate with a "knowledge-based" definition of intelligence. ...
... Indeed, as Macnamara and Burgoyne (2023) mention, many experiments have found significant effects of mindset interventions on academic outcomes, but nonsignificant effects on reported mindset itself (e.g., Balan & Sjöwall, 2022;Miller & Srougi, 2021). Seeing as a previous meta-analysis by Burgoyne, Macnamara, and colleagues found larger effects of mindset interventions when manipulation checks have failed (Sisk et al., 2018), the totality of evidence suggests that the association between intervention outcomes on achievement and mindset beliefs is dissociable (or at least not very well correlated). ...
... Explanatory theories, like growth mindsets, are critical for the advancement of educational psychology, but parsimonious explanations can lead to poor predictions (Shmueli, 2010). Meta-analyses have shown that a growth mindset might be influential over time (Funder & Ozer, 2019) but only modestly predicts achievement (r = 0.10; Sisk et al., 2018). As described in other articles of this special issue (e.g. ...
... Teachers can foster a growth mindset by teaching students about the malleability of ability or by praising students for their effort and improvement, rather than their abilities or talents (Dweck, 2008). Across subject areas, educational interventions to build a growth mindset have, on average, small long-term effects (Sisk et al., 2018). However, in physical activity and sport, meta-analyses have shown that having a growth mindset is reliably connected with adaptive outcomes (e.g. ...
... Similarly, bad predictors are not necessarily unimportant. As mentioned earlier, both mindset theory and achievement goal theory have systematic reviews demonstrating how constructs from these frameworks influence students (Biddle et al., 2003;Jaitner et al., 2019;Ntoumanis & Biddle, 1999;Sisk et al., 2018). One explanation for why those behaviours were not consistently selected in our models is that the effects may be relatively smaller than those of other teacher behaviours (Sisk et al., 2018). ...
Educational psychology usually focuses on explaining phenomena. As a result, researchers seldom explore how well their models predict the outcomes they care about using best-practice approaches to predictive statistics. In this paper, we focus less on explanation and more on prediction, showing how both are important for advancing the field. We apply predictive models to the role of teachers on student engagement, i.e. the thoughts, attitudes, and behaviours, that translate motivation into progress. We integrate the suggestions from four prominent motivational theories (self-determination theory, achievement goal theory, growth mindset theory, and transformational leadership theory), and aim to identify those most critical behaviours for predicting changes in students’ engagement in physical education. Students (N = 1324 all from year 7, 52% girls) from 17 low socio-economic status schools rated their teacher’s demonstration of 71 behaviours in the middle of the school year. We also assessed students’ engagement at the beginning and end of the year. We trained elastic-net regression models on 70% of the data and then assessed their predictive validity on the held-out data (30%). The models showed that teacher behaviours predicted 4.39% of the variance in students’ change in engagement. Some behaviours that were most consistently associated with a positive change in engagement were being good role models (β = 0.046), taking interest in students’ lives outside of class (β = 0.033), and allowing students to make choices (β = 0.029). The influential behaviours did not neatly fit within any single motivational theory. These findings support arguments for integrating different theoretical approaches, and suggest practitioners may want to consider multiple theories when designing interventions. More generally, we argue that researchers in educational psychology should more frequently test how well their models not just explain, but predict the outcomes they care about.
... Another question that we are interested in is the boundary condition of the growth mindset. Although the benefits of a growth mindset have already been widely recognized [9,10], researchers have also challenged whether its positive effect applies to students who already feel confident about their abilities [11]. POQ students meet this criterion perfectly. ...
... Although educators have already started to apply mindset theory in their practice [40], a recent meta-analysis suggested that the relationship between growth mindset and academic achievements (r = 0.10), as well as the effect of growth mindset intervention on performance improvement (d = 0.08), is rather weak [11]. The authors offered a hypothesis to explain this result: a growth mindset may be less beneficial or even detrimental for students who are already confident about their abilities because a growth mindset may make their advantages fragile. ...
... If interventions can help students with a growth mindset be motivated by POQ as much as students with a fixed mindset, their academic performance will be further improved. Despite that, this finding suggests that intervention with a growth mindset may be less effective for students who are already confident in their abilities, which is consistent with the findings of a meta-analysis [11]. Considering the fact that such interventions have been widely adopted [38], we suggest practitioners focus more on disadvantaged groups when adopting such interventions. ...
School psychologists are concerned about underperforming students; however, a recent study calls attention to a group of college students who believe themselves to outperform other students: students who perceive themselves as overqualified. In this study, we revisited the double-edged sword model of college students’ perceived overqualification (POQ) by untangling the mediating mechanism between POQ, learning engagement, and life satisfaction. We also tested the interactions between the growth mindset and POQ. Two questionnaire surveys were conducted and attained some different results from previous studies: (1) POQ positively predicted learning engagement via the mediation of career aspiration and performance-approach goals but not performance-avoidance goals; (2) The positive effects of POQ on career aspirations, performance-approach goals, and learning engagement were weakened by the growth mindset; (3) The relationship between POQ and life satisfaction was nonsignificant. Relative deprivation negatively mediated this relationship, while generalized self-efficacy positively mediated this relationship. These findings enriched our understanding of how POQ may affect college students’ learning and well-being; in addition, we also provided initial evidence that a growth mindset is less beneficial for members of advantaged groups in academic settings. Based on our findings, we offered practical suggestions regarding POQ students in colleges.
... The intervention has been adapted to and tested in high-income countries, including a nationally representative sample in the UStA , Norway (Bettinger et al., 2018), and the UK (Donohoe et al., 2012). This 2-session intervention was shown to successfully strengthen the students' belief that intellectual ability can be developed over time, i.e., helped to increase student orientation towards a growth mindset in regards to intelligence, while simultaneously benefiting academic achievement and fostering their propensity to seek out challenges (Bettinger et al., 2018;Sisk et al., 2018;Yeager et al., 2019). This has been shown to be particularly beneficial among low-income and high-risk learners (Bosch, 2021;Sarrasin et al., 2018;Yeager et al., 2019). ...
... However, the relationship between mindset and gender stereotyping remains unclear (Law et al., 2021), and there are a number of conceptual intelligence issues that underlie the relationship between gender, mindset, and academic achievement that are also not broadly understood (Costa & Faria, 2018). In turn, the differential effect of interventions on growth mindset as a function of gender is also unclear, with the exception of certain evidence that suggests it is more beneficial for young males than young females (Sisk et al., 2018). ...
... However, the findings in this study do not allow us to reach the same conclusion. There remains much to learn about heterogeneity in growth mindset interventions (Yeager & Dweck, 2020), and these findings build on the limited research conducted into the heterogeneous effect of mindset interventions by gender and initial mindset (Sisk et al., 2018). ...
Computer-based interventions that aim to help students endorse a growth mindset have been designed and tested in high-income countries for a number of years. However, there is no evidence of their effectiveness in middle-income nations. In those studies, students’ growth mindset has traditionally been measured using surveys where students report the extent to which they believe intelligence is fixed or malleable, without linking intelligence with a more specific dimension, such as math or language. In addition, these measurements have been undertaken without distinctions being made between personal ability (“my” intelligence) and more general abilities (everyone’s intelligence). Therefore, by means of a randomized experiment, this study assesses the impact of a single-session online growth-mindset intervention in Chile on distinct measurements of the growth mindset of students (general, personal, and subject-specific), as well as their propensity to seek out challenges. Accordingly, a sample of 248 students was recruited from 9 and 11th grades in three secondary schools, all of whom were randomly assigned to either a treatment or control group. The intervention was found to increase their propensity to seek out challenges and to experience an increase in growth mindset scores in all tested dimensions. No evidence of the heterogeneity of results by gender or prior growth mindset was identified.
... In a study involving 15-year-old students, Yan et al. (2021) found that GM exerted a significant direct effect (β = 0.20, p < 0.001) in Western countries (such as the US, the UK, Ireland, and Australia), but a non-significant effect (β = − 0.02, p > 0.05) in Confucian countries (such as mainland China, Hong Kong SAR, Macao SAR, Chinese Taipei, Japan, and Korea). Sisk et al. (2018) conducted a meta-analysis and found that the average positive correlation between GM and academic achievement was generally weak (r = 0.10, k = 273, N = 365,915, with studies selected from 1989 to 2016). Some studies have even found no correlation between GM and learning achievement (e.g., Cho et al., 2021;Wanzek et al., 2020). ...
... Echoing many previous studies, we did not find a direct effect of GM on L2 writing. As Sisk et al. (2018) found in their meta-analysis, GM was only weakly correlated with academic achievement. Similar to many other studies focusing on areas such as reading comprehension (β = 0.01 from Cho et al., 2021; β = − 0.02 from Yan et al., 2021), we found no direct effect of GM on writing performance. ...
A growth mindset (GM), defined as an individual’s perception that their intellectual ability is malleable, has been the subject of extensive research attention, as it can facilitate learning in many contexts. GM has been found to have more pronounced positive effects on students with lower-level writing proficiency. Emotions have also been found to play a significant role in second language (L2) writing. We conducted an innovative investigation of the relationships between GM, emotions related to writing (enjoyment and anxiety), and writing performance. The results of our study involving 589 Chinese 12th-graders and L2 writing tasks showed that GM was positively associated with enjoyment and negatively associated with anxiety. When assessing students grouped according to their writing performance (high, middle, and low), we found an indirect positive path from GM to writing performance via anxiety in the middle-level group and via enjoyment in the low-level group. The findings suggest that GM can promote enjoyment and mitigate anxiety, therefore facilitating L2 writing performance. The pedagogical implications are that teachers should encourage students to develop a GM and foster their social–emotional learning.
... The detractors of Dweck's implicit theory of intelligence argue the model is seemingly onedimensional and dualistic (Graham, 1995;Harackiewicz & Elliott, 1995), has not considered other factors such as stability of intelligence over time, hereditary and environmental factors (Gelman, Heyman, & Legare, 2007;Gottfried, Gelman, & Schultz, 1999;Graham, 1995;Haslam, Bastian, & Bissett, 2004;Haslam, Bastian, Bain, & Kashima, 2006) and contend that a growth mindset is not enough, claiming a third mindset of deliberate practice is needed (Ericson & Pool, 2016). Critics claim that the proponents of growth mindset research have overstated findings in papers, books and the popular press, talking of the mindset revolution (Sisk, Burgoyne, Sun, Butler, & Macnamara, 2018). Sisk, Burgoyne, Sun, Butler, and Macnamara (2018) undertook two meta-analyzes of the literature reviewing 273 and 43 studies respectively to examine the effectiveness of mindset interventions on academic achievement and potential moderating factors. ...
... Critics claim that the proponents of growth mindset research have overstated findings in papers, books and the popular press, talking of the mindset revolution (Sisk, Burgoyne, Sun, Butler, & Macnamara, 2018). Sisk, Burgoyne, Sun, Butler, and Macnamara (2018) undertook two meta-analyzes of the literature reviewing 273 and 43 studies respectively to examine the effectiveness of mindset interventions on academic achievement and potential moderating factors. The findings indicated a weak correlation (0.19) between a growth mindset and academic achievement with the average effect size for educational interventions was 0.57. ...
“I think I can, I think I can’ puffed ‘The Little Engine That Could’. The American folktale taught the value of optimism and hard work reflecting a growth mindset belief about abilities. A growth mindset positively impacts academic achievement, motivation, and children’s agency for learning. Few studies have explored how early childhood teachers can develop children’s growth mindsets. We report on a study that developed design principles to assist early childhood teachers to foster a growth mindset in children in early childhood classrooms at one school in Western Australia using design-based research. Two iterations of the principles were designed and examined with teachers of children aged 3.5 years to 6.5 years of age. During three focus groups conducted at the beginning, middle and end of two iterations, the researcher and teachers collaboratively developed, reflected, and refined the principles. Weekly video diaries recorded the participant’s reflections on the principles. The nine principles were found to improve early childhood teacher knowledge and practice to foster a growth mindset in children. The results from this study contribute theoretical and practical knowledge to support the inclusion of mindset theory in early childhood contexts to foster children’s growth mindset for positive learning outcomes.
... Although notable claims have been made, the extent to which efforts to shift students' mindsets from fixed to growth have transferable effects on their learning performances remains a subject of ongoing debate. One major source of criticism comes from a study containing two meta-analyses by Sisk et al. (2018) which examined the strength of the relationship between mindset and academic achievement and the effectiveness of mindset interventions on academic achievement. In the first meta-analysis, the researchers analysed 129 studies and concluded that the correlation between a growth mindset and academic achievement was very weak, with an average meta-analytic correlation of r = 0.10. ...
... Previous reviews and meta-analyses have highlighted the associations of implicit theories with self-regulation theory (Burnette et al., 2013) and focused on the effects of teaching neuroplasticity to induce a growth mindset on motivation and achievement (Sarrasin et al., 2018). Studies have examined the strength of the relationship between mindset and academic achievement and the effectiveness of mindset interventions on academic achievement (Sisk et al., 2018). They have modelled the link between ITI and students' academic achievement in different domain-specific subjects (verbal and quantitative) with consideration of relevant factors such as socio-demographic and cultural moderators (Costa & Faria, 2018) or surveyed how mindsets have been studied among teachers and students (Zhang et al., 2017). ...
A growing body of research has suggested that mindset is one powerful predictor of students' academic achievement and that students are likely to hold self-beliefs about the malleability or stability of their academic abilities. In the domain of mathematics education, a belief in ‘math brain’ – as something you do or do not possess – is widely prevalent. Studies have shown that teachers and students are more likely to consider achievement in mathematics than achievement in other academic domains to be due to inborn ability. Most growth mindset-related research in schools is domain-general; however, given the prevalence of strong beliefs about the innateness of mathematical ability, possible idiosyncratic effects of mindset interventions in the mathematics domain may have been overlooked by research reviews and meta-analyses that do not examine domain-specific effects. The purpose of this paper is to compile and synthesise quantitative and qualitative research on interventions in mathematics classrooms that aim to change or foster teachers' and/or students' beliefs/mindset in primary and secondary schools and the reported impacts of these interventions (16 studies). The interventions in these studies were identified and sorted based on their targets (teacher-focused or student-focused), content (implicit theories of intelligence (ITI) intervention for general domains or in mathematics domain), and delivery mode (technology-based or in-contact). The results suggested most of the considered studies were quantitative and used student-focused interventions. Moreover, when ITI interventions were conducted specifically in the mathematics domain, positive results were reported, regardless of the intervention target or content or mode of delivery, whereas general ITI interventions yielded mixed results. Future studies should therefore consider the impacts of domain-specific effects and intervention targets when designing mindset interventions.
... Dweck, 2017). Research has indicated that a growth mindset (beliefs that human attributes can be developed with dedication and work) has a positive effect on interpreting and solving learning tasks, learning strategies, achievement, and coping with failure (Sisk et al., 2018). These factors also can have an inhibiting effect on various mental health problems (Burnette et al., 2020). ...
... The effects of mindset interventions overall are small. However, students with low socioeconomic status and students who are academically at risk, benefit comparatively more from these interventions (Sisk et al., 2018). ...
The importance of students' learning to learn competence for academic achievement, as well as their well‐being at school and in life, is increasingly emphasised by educators and policy makers in national curricula and educational strategies. In an uncertain and complex world, learners need to become autonomous, be able to analyse challenges and apply knowledge in different contexts, address complex tasks, and create new knowledge. This article explores concepts and approaches to the development of students' learning to learn competence in the context of education in Estonia. First, the conceptualisation, model and dimensions of learning to learn competence are described and related challenges for teachers are analysed. Second, an overview of Estonian teachers' current practices, beliefs, knowledge, skills and occupational standards relevant to students' learning to learn competence is provided. We discuss how Estonian teacher education policy may enhance or inhibit the work of teachers when supporting students to develop learning to learn competence. Future directions for teacher educators and how to prepare teachers to support the development of students' learning to learn competence are suggested.
... A recent study showed that adolescents who received a growth mindset intervention showed higher math grades a year after the intervention (Janssen and van Atteveldt, 2022). This effect was relatively large (d = 0.36) compared to the literature (see Sisk et al., 2018). Nevertheless, Janssen and van Atteveldt (2022) could not establish a diminishing effect of the growth mindset intervention on school burnout symptoms, which might take longer to manifest. ...
... The second limitation was that our sensitivity analyses indicated that we only had enough power to detect medium to large effects. Although it is not uncommon in vagal activity research to find medium effects (Brindle et al., 2014), studies investigating mindset generally find small effects (Sisk et al., 2018). It is therefore possible that our subsample was underpowered. ...
Experiencing school burnout symptoms can have negative consequences for learning. A growth mindset, the belief that human qualities such as intelligence are malleable, has previously been correlated with fewer school burnout symptoms in late adolescents. This might be because adolescents with a stronger growth mindset show more adaptive self-regulation strategies and thereby increasing resilience against academic setbacks. Here we confirmed in a sample of 426 Dutch young adolescents (11–14 years old; 48% female) that this relationship between growth mindset and school burnout symptoms holds after controlling for other potential predictors of school burnout symptoms such as academic achievement, school track, gender, and socio-economic status. Our second aim was to increase our understanding of the mechanism underlying the relation between mindset and school burnout, by measuring physiological resilience (vagal activity, a measure of parasympathetic activity, also known as heart rate variability or HRV) in a subsample ( n = 50). We did not find any relation between vagal activity and growth mindset or school burnout symptoms, nor could we establish a mediating effect of vagal activity in their relation. In conclusion, we found evidence for a potential protective effect of a growth mindset on school burnout symptoms in young adolescents, but not for physiological resilience (vagal activity) as an underlying mechanism. The protective effect of growth mindset as confirmed in our younger sample can be leveraged in interventions to prevent increasing school burnout symptoms.
... Fostering socio-emotional skills-which are often conceptualized according to the Big Five framework of personality traits )-through school-based programs and similar interventions has been welcomed as a possible conduit for improving students' academic achievement and life outcomes more generally (e.g., Sánchez Puerta et al., 2016;Bleidorn et al., 2019;Malanchini et al., 2019). Both researchers and policymakers have espoused the hope that fostering socio-emotional skills particularly among socially disadvantaged students might be a way to reduce social inequality in academic achievement and related outcomes Arias et al., 2017;Sisk et al., 2018;Grosz et al., 2021). ...
... The other three Big Five traits had smaller and more varied associations with achievement-the correlation between Agreeableness and achievement, for example, was r = 0.07 [but see Brandt et al. (2020)]. These findings have stimulated interventions to improve student outcomes by fostering traits conducive to school achievement-particularly traits from the Conscientiousness family-thus far with mixed success (Arias et al., 2017; but see Alan and Ertac, 2018;Alan et al., 2019, andSisk et al., 2018, for encouraging findings). ...
How temperament is in relation to school readiness
... In general, mindsets subsume the way learners perceive the challenges they face in everyday life, the obstacles they must overcome, the efforts they have to make to achieve their goals, the criticisms they receive, and the success of others (Dweck, 2020). However, mindset theory was heavily criticised and led to controversial results in recent studies (Foliano et al., 2019;King & Trinidad, 2021;MacNamara & Rupani, 2017;Moreau et al., 2018;Sisk et al., 2018). These studies found that mindset has a poor association with achievement and academic outcomes, either by a lack of significant correlation between the two constructs or by showing only weak correlations. ...
... In addition to the alpha and omega values, CFA-specific construct reliability and construct validity (convergent validity) measures were computed, and the average variance extracted (AVE) and composite reliability (CR) coefficients showed that all of the measures are acceptable (Fornell & Larcker, 1981;Shrestha, 2021). Consequently, the measures were considered to be reliable for their use in this study except for the Growth L2 mindset and Fixed L2 mindset scales, which may lend additional support to the criticism around mindset theory (Foliano et al., 2019;King & Trinidad, 2021;MacNamara & Rupani, 2017;Moreau et al., 2018;Sisk et al., 2018). ...
https://ppk.elte.hu/dstore/document/1217/Z%C3%83%C2%B3lyomi%20Anna_t%C3%83%C2%A9zisf%C3%83%C2%BCzet_angol.pdf
... A small industry offering growth mindset interventions has flourished in recent years. The interventions typically aim to teach a growth mindset by explaining the concept through reading, presentation, or an interactive game (Sisk et al. 2018). For example, MindsetWorks, LLC, sells a growth mindset intervention computer program, "Brainology", that teaches students that intelligence can be developed with effort using lessons, online reflections, and activities. ...
... For example, MindsetWorks, LLC, sells a growth mindset intervention computer program, "Brainology", that teaches students that intelligence can be developed with effort using lessons, online reflections, and activities. Mindset's popularity has been described as a "revolution that is reshaping education" (Boaler 2013, p. 143), with growth mindset interventions being implemented in classrooms around the world (Sisk et al. 2018). ...
Mindset theory assumes that students' beliefs about their intelligence-whether these are fixed or can grow-affects students' academic performance. Based on this assumption, mindset theorists have developed growth mindset interventions to teach students that their intelligence or another attribute can be developed, with the goal of improving academic outcomes. Though many papers have reported benefits from growth mindset interventions, others have reported no effects or even detrimental effects. Recently, proponents of mindset theory have called for a "heterogeneity revolution" to understand when growth mindset interventions are effective and when-and for whom-they are not. We sought to examine the whole picture of heterogeneity of treatment effects, including benefits, lack of impacts, and potential detriments of growth mindset interventions on academic performance. We used a recently proposed approach that considers persons as effect sizes; this approach can reveal individual-level heterogeneity often lost in aggregate data analyses. Across three papers, we find that this approach reveals substantial individual-level heterogeneity unobservable at the group level, with many students and teachers exhibiting mindset and performance outcomes that run counter to the authors' claims. Understanding and reporting heterogeneity, including benefits, null effects, and detriments, will lead to better guidance for educators and policymakers considering the role of growth mindset interventions in schools.
... Despite some well-publicized successes with some interventions, a recent meta-analysis by Sisk et al. [42] revealed that the effectiveness of mindset interventions varies significantly, with only 12% of included interventions significantly improving academic achievement. One possible factor that could determine the effectiveness of a mindset intervention is the demographic groups a student belongs to. ...
Students’ motivational beliefs, such as disciplinary intelligence mindsets, can influence their physics performance and persistence. Intelligence mindset beliefs have long been argued to fall along of continuum between fixed and growth mindsets. Those with fixed physics mindsets believe that ability in physics is innate and unchangable, while those with growth mindset believe that ability in physics can be developed with effort. More recent research with physical science and engineering majors suggests these are somewhat separable beliefs, with some students believing aspects of both fixed and growth mindsets, and that students can hold different beliefs about typical other students versus beliefs about themselves (e.g., others could improve through effort but they themselves could not). In this study, 419 students in physics 1 for students pursuing bioscience majors took pre- and post-physics mindset surveys. We investigated whether the physics mindset views of students pursuing bioscience or health-related majors were separable into more nuanced dimensions, if the means and distribution of these views varied by gender or sex and over time, and if any of these views predicted course grade. Replicating prior findings with physical science and engineering majors, we found that intelligence mindsets can be divided into four separable but correlated constructs: my ability, my growth, others’ ability, and others’ growth. Further, in this bioscience or health-related majors group, the “ability” beliefs grew stronger and the “growth” beliefs became weaker over time. These shifts were particularly strong for women. The changes in beliefs were also stronger for “my” beliefs than “others” beliefs for both men and women Unfortunately, my ability and my growth scores were also the strongest predictors of course grades above and beyond academic preparation differences as assessed by high school GPA and SAT/ACT math scores. These findings have implications for eliminating classroom inequities, such as through the development of new mindset interventions.
... Burgoyne, Hambrick, & Macnamara, 2020;Sisk et al., 2018). However, future study is required to investigate the limits of the effectiveness of growth mindset interventions since there is a persistent fear that all of these efforts and interventions will be ineffective in the long run. ...
Students with learning disabilities have increased continuously from last few decades’ due to this threat of emerging academic, social, and psychological issues increased automatically. Students who have a fixed mindset that their qualities are fixed, whereas those who have a growth mindset that their qualities are flexible. The current applied research investigated Pakistan's Superior University the efficiency of a Growth Mindset intervention based on (Dweck, Chiu, & Hong, 1995) theory. Through project-based learning, this study indicates to determine how an intelligence scale and learning disabilities affect students' growth mindsets. Results of Before Intervention Test (BIT) were ineffective, so an After Intervention Test (AIT) was implemented through a project-based learning activity, and significant results were discovered. The validity and reliability tests were employed in this work to support the analysis of multiple linear regression. The findings of this study indicate that student growth attitude is highly influenced by both learning disability (X2) and IQ scale (X1), as well as by all other variables taken together. If researchers, administrators, teachers, and other people have a better knowledge of the mindsets of students with learning disabilities, they will be better able to develop effective solutions.
... Individuals approach cognitive tasks differently depending on their implicit theories of intelligence 17,28,[38][39][40][41][42] . In educational settings, those who endorse an entity theory of intelligence emphasize the performance of intelligence 43 . ...
The present study examined how taking a general knowledge (GK) test affects perceptions of one’s intelligence, memory, and knowledge and the relationship between these three constructs. Participants rated their abilities on each construct and the strength of the relationships between them before and after completing an easy or hard GK test or control task. In Experiment 1, participants were (mis)informed that GK questions were correctly answered by 50% of the population; in Experiment 2, no such information was provided. Regardless of (mis)information about others’ performance, participants in the Hard condition believed they had a worse memory, were less knowledgeable, and were less intelligent post-task. However, the strength of the perceived relationship between GK and intelligence decreased only when participants were misled. Judgments of one’s intelligence, memory, and knowledge can be manipulated by taking a GK test, and individuals engage in self-protective behavior to reduce the potential threat to one’s self-concept.
... Conversely, adolescents with fixed mindsetsdor the belief that intelligence is an inherited and unchangeable qualitydmay interpret failure as an indication that they are not smart enough to succeed, which in turn contributes to learning disengagement. While there has been some noted success in improving student academic outcomes via mindset interventions (e.g., Yeager et al., 2019), meta-analytic findings have indicated weak overall effects (Sisk et al., 2018). In response, researchers have postulated that heterogeneity across learning environmentsdthat is, variability in classrooms' social and cultural contextsdis likely responsible for these mixed findings (Yeager et al., 2022;Yeager and Dweck, 2020). ...
For three decades, researchers have examined youth’s interactions with learning activities through the holistic construct of
engagement. Recently, motivation scholars have defined engagement as a multidimensional construct with behavioral,
emotional, cognitive, and social components. This article presents an integrative developmental-contextual perspectivedthe
Development-in-Sociocultural-Context (DISC) Model of Learning Engagementdthat positions engagement as the product
of dynamic developmental and relational processes within youth’s social, cultural, and historical contexts. The conceptualization
and study of engagement as a multidimensional construct subject to individual and contextual influences necessitates
the identification of the specific developmental, contextual, historical, and sociocultural factors that foster or
undermine engagement.
... Bemovou studií to bohužel nekončí. Mezi ty mediálně nejznámější patří studie na základě zcela vymyšlených výsledků od Stapela 1 a Lindenberga (2011), za kterou byla hlavnímu autorovi pozastavena profesura na univerzitě (Levelt Committee, 2012), nebo studie, jejíž výsledky se následně ukázaly s výrazně nižším efektem, jako byla Carlo Dweck 2 (2006) a její "mind set" (Sisk et al., 2018), či několikrát nepotvrzené výsledky Amy Cuddy 3 (Carney et al., 2010) jejíž videa s tzv. "Power posing" vidělo přes 67 milionů lidí (Cuddy, 2012). ...
... Although we observe improvements in MAI scores, similar to other educational interventions (e.g., interventions aimed at fostering a growth mind set to improve academic achievement; Sisk et al., 2018), the size of these effects tends to be small (for a discussion, see Kraft, 2020). On average, we observe a 1.9point change on the MAI scale, which would indicate that LAs are endorsing one or two items as being "more typical of them" as learners after participating in the program. ...
Learning assistant (LA) programs train undergraduate students to foster peer discussion and facilitate active-learning activities in undergraduate science, technology, engineering, and mathematics (STEM) classes. Students who take courses that are supported by LAs demonstrate better conceptual understanding, lower failure rates, and higher satisfaction with the course. There is less work, however, on the impact that participating in LA programs has on the LAs themselves. The current study implements a pretest-posttest design to assess changes in LAs' metacognition and motivation to succeed in STEM across their first and second quarters as an LA. Our findings suggest that participating in this program may help LAs become more reflective learners, as was demonstrated by an increase in their scores on the Metacognitive Awareness Inventory (MAI) after the first quarter. LAs also showed increases on the Intrinsic Motivation and Self-Efficacy subscales of the Science Motivation Questionnaire. Students who participated in the program for an additional quarter continued to show increases in their MAI scores and maintained the gains that were observed in their motivation. Taken together, this work suggests that, in addition to benefiting the learner, LA programs may have positive impacts on the LAs themselves.
... For example, growth mindset interventions-which guide students to see that their abilities are not set in stone but can be developed-are especially beneficial to students with more negative self-views (Thomaes et al., 2020) and students from disadvantaged backgrounds, thus reducing achievement inequality (Yeager et al., 2016). A meta-analysis established that growth mindset interventions improve academic achievement among students from low-SES (but not high-SES) backgrounds (Sisk et al., 2018; also see Luthar et al., 2020). ...
Children from low socioeconomic status (SES) backgrounds often have more negative self-views than their peers. How are these self-views shaped by teacher-student interactions in the classroom, and what are the consequences of these self-views for achievement inequality? We present a developmental framework addressing these questions by bridging insights from the psychological, educational, and sociological literatures. We show that children from low-SES backgrounds perceive themselves as less intelligent, less able to grow their intelligence, less deserving, and less worthy, independent of their actual abilities and achievements. We demonstrate how negative intellectual stereotypes-expressed through daily interactions with teachers in classrooms, such as teachers' expectations, feedback, and attention-undercut the self-views of children from low-SES backgrounds. We also show how this process can be exacerbated by institutional and cultural values reflecting a belief in meritocracy (e.g., schools that encourage competition, emphasize raw ability, and attribute achievement inequality to intrinsic factors), which are common in countries with high income inequality and rigid between-school tracking. The ensuing more negative self-views introduce psychological barriers that undermine the academic achievement of children from low-SES backgrounds, thereby reinforcing achievement inequality. This represents an enormous loss of potential and perpetuates harm into adulthood. Socioeconomic disparities in self-views can emerge early in life and widen with age, underlining the need for developmental research and timely intervention. We discuss implications for studying the nature, origins, and consequences of socioeconomic disparities in self-views, and for designing interventions to reduce achievement inequality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
... Further, students can explicitly identify the ideas they were struggling with and continue to work on them until they attain mastery. This overall evidence of a transition to a growth mindset is another, though indirect, measure of our potential at supporting underrepresented students, as adoption of growth mindsets in classrooms has been shown to have a positive impact on student success, particularly for groups that are traditionally underrepresented (Hill et al., 2010;Sisk et al., 2018). ...
Justice Through the Lens of Calculus is a freely available MAA Notes Volume for anyone interested in building a more diverse, equitable, and inclusive math environment into their teaching and departmental practices. The volume contains case studies from over 30 institutions along with 8 cross-cutting thematic chapters written by math education researchers. Rather than simply listing best practices, the volume presents struggles, challenges, opportunities and achievements from our colleagues as well as theoretical frameworks and approaches to help us thoughtfully consider the impact of our intent. This volume is well-suited for discussion groups, professional development, workshops or courses on pedagogy. We encourage readers to reach out to the authors to learn more and foster dialogue within our community to improve mathematics for everyone!
... Studies evaluating the use of Growth Mindset in schools have focused on how it can impact academic attainment (Li & Bates, 2019). Two meta-analyses by Sisk et al. (2018) identified weak effects of Growth Mindset on academic outcomes. Growth Mindset has not been evaluated as a whole-school intervention to improve pupil well-being. ...
The incorporation of mental well‐being provision into school curricula is increasingly the focus of government policy in the UK and internationally. However, it is not clear what well‐being programmes schools provide to pupils, and how these programmes are delivered. The current study was an online survey to assess the use of whole‐school well‐being programmes in primary schools in North Wales. Normalisation Process Theory was utilised as a framework to assess normalisation of the well‐being programmes. One‐hundred and fifty‐one schools in North Wales responded to the survey. The mean number of whole‐school well‐being programmes utilised by schools was 4.59, and nine of the 10 most frequently used programmes had little or no associated evidence base. The well‐being programmes were generally perceived as normalised (i.e. everyday practice) by respondents. Implications for future practice are discussed, including the need to support schools to identify and implement evidence‐based mental well‐being provision.
... During this research, we kept in mind that there are also studies that show that mindset interventions in general are not the magic silver bullet everyone is talking about. For example, Sisk et al. (2018) found, based on two large analyses, that there were only very weak correlations between mindset and academic achievement. An interactive mindset intervention, including reading and discussing, seemed to have only a small effect on academic achievement. ...
... For example, considering students who do poorly on the initial mastery assignments, do students with a growth mindset perform better on subsequent assignments compared to students with a fixed mindset? We will also consider how overall performance on all mastery assignments is related to the growth mindset, though correlations between mindset and measures of academic achievement, such as exam scores, have been found to be positive but very weak and with wide variation [31,32]. ...
We have investigated the temporal patterns of algebra (N=606) and calculus (N=507) introductory physics students practicing multiple basic physics topics several times throughout the semester using an online mastery homework application called science, technology, engineering, and mathematics (STEM) fluency aimed at improving basic physics skills. For all skill practice categories, we observed an increase in measures of student accuracy, such as a decrease in the number of questions attempted to reach mastery, and a decrease in response time per question, resulting in an overall decrease in the total time spent on the assignments. The findings in this study show that there are several factors that impact a student’s performance and evolution on the mastery assignments throughout the semester. For example, using linear mixed modeling, we report that students with lower math preparation for the physics class start with lower accuracy and slower response times on the mastery assignments than students with higher math preparation. However, by the end of the semester, the less prepared students reach similar performance levels to their more prepared classmates on the mastery assignments. This suggests that STEM fluency is a useful tool for instructors to implement to refresh student’s basic math skills. Additionally, gender and procrastination habits impact the effectiveness and progression of the student’s response time and accuracy on the STEM fluency assignments throughout the semester. We find that women initially answer more questions in the same amount of time as men before reaching mastery. As the semester progresses and students practice the categories more, this performance gap diminishes between males and females. In addition, we find that students who procrastinate (those who wait until the final few hours to complete the assignments) are spending more time on the assignments despite answering a similar number of questions as compared to students who do not procrastinate. We also find that student mindset (growth vs fixed mindset) was not related to a student’s progress on the online mastery assignments. Finally, we find that STEM fluency practice improves performance beyond the effects of other components of instruction, such as lectures, group-work recitations, and homework assignments.
... (Destin et al., 2019, p. 2). As a result, evidence suggests that marginalized and less experienced individuals living in persistent poverty are more likely to possess a "fixed-mindset," which is a tendency to view their own set of knowledge and skills as relatively inflexible (Claro et al., 2016;Dweck, 2015;Sisk et al., 2018;Wuepper & Lybbert, 2017). ...
Research Summary
Although entrepreneurship training programs are designed to help necessity entrepreneurs acquire skills and capabilities to take entrepreneurial action, participants in these programs often fail to do so. In partnership with a local government agency, we conducted a randomized field experiment involving 165 entrepreneurs in rural Tanzania where in addition to providing technical‐skills training, approximately half of the participants also received “growth mindset” psychological training. Those who received the growth mindset training displayed more entrepreneurial action in their business than those in the control group. Importantly, higher levels of entrepreneurial self‐efficacy mediated the positive impact on entrepreneurial action displayed by participants who received the growth mindset training. We discuss how complementing traditional technical‐based training with growth mindset training can improve the efficacy of entrepreneurship training programs.
Managerial Summary
Entrepreneurship training programs often fall short in translating knowledge into action. To address this issue, we conducted an experiment with 165 entrepreneurs in rural Tanzania. All participants received technical‐skills training, but half were also exposed to “growth mindset” training. Those who received the growth mindset training displayed greater initiative in business growth. The newfound confidence and grit they gained empowered them to apply learned principles effectively, ultimately enhancing the effectiveness of entrepreneurship training programs.
... There has been controversy about the impact of these beliefs on motivation and learning outcomes. Several researchers observed that a fixed mindset predicted lower motivation, negative emotions, and lower achievement (e.g., Blackwell, Trzesniewski, & Dweck, 2007;Haimovitz, Wormington, & Corpus, 2011;King, 2016;Park, Gunderson, Tsukayama, Levine, & Beilock, 2016), whereas others found mindsets to be only weakly related or unrelated to achievement (e.g., Burgoyne, Hambrick, & Macnamara, 2020;Sisk, Burgoyne, Sun, Butler, & Macnamara, 2018). Nevertheless, there seems to be consensus that mindsets, despite not being a key achievement predictor, can influence people's reactions when they fail or face challenges. ...
... First is self-efficacy, used here not only in its narrower technical sense but also as an umbrella for the other competence and control-related constructs discussed previously. Second is a growth mindset (i.e., whether students believe these capacities can develop and improve through the application of effort, practice, and the acquisition of effective strategies) which provides a crucial motivational backdrop for understanding the meaning of ability and competence (Burnette et al., 2020;Sisk et al., 2018; Yeager et al., 2019). Third, are mastery goals (Elliot & Hulleman, 2017), taken from achievement goal theory in its broad original meaning, which refer to students' understanding and definition of success and the purpose of schooling-in this case, focused on "engagement with the purpose of developing competence through learning and developing skills" (Urdan & Kaplan, 2020, p. 2). ...
The field of achievement motivation is concept and data rich, housing more than a dozen major theories, all of which have withstood empirical scrutiny. Their very success, however, has enabled them to flourish within siloed territories. Such fragmentation creates major problems for educators, interventionists, and researchers entering the field. They are faced with a splintered and confusing picture of student motivation. Researchers new to the field find it difficult to see the commonalities or compare the differences across theories. Interventionists cannot design comprehensive educational programs, nor can teachers form coherent mental models of the field. This paper offers four guideposts to aid in the principled integration of motivational theories: (1) motivational resilience, an umbrella construct encompassing the core observable features of motivation—the energy, direction, and durability of action; (2) academic identity, which provides common ground for the many self-systems featured in motivational theories; (3) complex social ecologies, which serve as a home for motivationally-relevant features of classrooms and other important contexts, and the higher-order meso- and macrosystems that pervade them; and (4) developmental embeddedness. Together, these organizational guideposts sketch the outlines of an overarching framework useful for mapping the place and function of core constructs from motivational theories. To a field that already provides a differentiated, dense, and detailed understanding of student motivation, integrative efforts would add the kind of comprehensiveness, coherence, and comprehensibility that can foster even greater theoretical and empirical progress and the design of even more effective educational interventions.
... Neither of these latter two effect sizes reached significance. These results do not substantively differ from the results of the prior metaanalytic synthesis of this team (Sisk et al., 2018). Burnette et al. (2023) find 53 studies across three outcomes (academic, mental health, and social). ...
Abstract
Some ideas just feel right, others not so much. Familiar ideas are easier to process, seem to enjoy broad support, and are more likely to be accepted. Culture-based familiarity with the gist of an idea enhances the sense that things are as they ought to be. An idea’s cultural fluency reduces the likelihood that people apply systematic rule-based reasoning strategies even when these would be appropriate. People shift to more skeptical reasoning strategies when ideas are unfamiliar and do not fit culture-based assumptions. This commentary applies a cultural fluency lens and a set of metascience principles to compare the meta-analytic syntheses of growth mindset interventions by Macnamara and Burgoyne (2022) and Burnette et al. (2022). In doing so, the commentary raises more fundamental questions about the relationship between theoretical claims, their popular acceptance, and the demanded level of evidence.
Public significance statement
When people consider an idea, policy, or theoretical claim, they often reason based on a gut sense that it feels right or does not. This gut feeling can be triggered by things external to whether the idea, policy, or claim really is right, including how familiar it seems. People have blind spots for the reasons behind their gut feelings so we outline ways scientists should go about testing and reporting to increase the chance that scientists and the public focus on the evidence and not gut based feelings.
... They also highlighted our finding that 94% of the included growth mindset interventions had differences between treatment and control conditions other than training a growth mindset. Taken together with previous findings that intervention effects are numerically larger when they fail to influence students' growth mindset than when they succeed (Sisk et al., 2018), Yan and Schuetze suggest that either growth mindset may not be the critical ingredient in growth mindset interventions, or that results may be due to measurement problems. Either way, the internal validity of growth mindset interventions is undermined, rendering the mechanism driving effects unclear. ...
Two meta-analyses examined the effects of growth mindset interventions. Burnette et al. (2023) tested two moderators and found that effects ranged from negative to positive. We (Macnamara & Burgoyne, 2023) tested 11 preregistered moderators and examined the evidence according to a well-defined set of best practices. We found major areas of concern in the growth mindset intervention literature. For instance, 94%
of growth mindset interventions included confounds, authors with a known financial incentive were two and a half times as likely to report positive effects, and higher quality studies were less likely to demonstrate a benefit. Yan and Schuetze (2023) contextualized these findings by describing problems with mindset theory and its measurement. Likewise, Oyserman (2023) discussed how growth mindset is a culturally fluent idea;
papers supportive of growth mindset are widely embraced, whereas papers taking a skeptical approach are challenged. In another commentary, Tipton et al. (2023) challenged our results, claiming to produce positive effects by reanalyzing our data set using Burnette et al.’s (2023) approach. However, in addition to changing the approach, Tipton et al. changed effect sizes, how moderators were coded, and which studies were included, often without explanation. Though we appreciate the discussion of multiple meta-analytic approaches, we contend that meta-analytic decisions should be a priori, transparently reported, and consistently applied. Tipton et al.’s analysis illustrated our (Macnamara & Burgoyne’s, 2023) conclusion: Apparent effects of growth mindset interventions on academic achievement may be attributable to inadequate study design, reporting flaws, and bias.
... For example, meta-analytic findings confirm that selfcontrol is a skill that can be taught (Friese et al., 2017). In addition, while meta-analytic results question the overall benefits of growth mindset interventions, moderator analyses reveal that growth mindset interventions do benefit children from low SES families (Sisk et al., 2018). As a result, future research might profitably be focused on identifying factors that serve as buffers and guide policymakers and practitioners in designing interventions that can block the negative effects of SES adversity on later leadership emergence. ...
We investigate the indirect effects of socioeconomic status, both at birth and at age 5, on the likelihood of holding a formal leadership position 26 years later via two sequential mechanisms: children's self-control at age 10 and adolescents' psychological well-being at age 16. We test this model using multisource data from the British Cohort Study, an ongoing research project studying individuals born in England, Scot-land, and Wales in the week of April 5-11, 1970. The data were collected at five different time points, from birth through early adulthood. Results show that the cumulative effects of early socioeconomic status predict children's self-control at age 10, and self-control in turn predicts a higher likelihood of leadership role occupancy at age 26 via psychological well-being at age 16. The findings of the current study illustrate how a range of individual and family factors measured across the lifespan predict leadership role occupancy, with implications for better understanding how socioeconomic adversity and privilege enhance individuals' likelihood of becoming leaders early in their careers.
... All of Ella's assessment tasks made minimal reference to theory, with routine or technical consideration (Ward & McCotter, 2004). The only theory Ella raised during conversation or interview was Dweck's (2017) highly critiqued (Sisk et al., 2018) theory of mindsets. In doing so Ella drew attention to her misunderstanding and application of the theory, stating the need to "convince" students to take on a growth mindset, without active pedagogical engagement in the development. ...
Lortie’s original conception of the apprenticeship of observation was one of conservative schooling practice and negative impact on teacher learning for school change. Schooling practices have changed in the 60 years since the original research that established the apprenticeship of observation as launch pad to teaching, changing prospective teachers’ experience of schooling. This article shares the experience of one pre-service teacher who never attended school, having been home educated. The absence of an apprenticeship of observation in schools highlights the need to better understand the growing diversity of experience pre-service teachers bring to initial teacher education. This one pre-service teacher’s experience raises the need to explore Lortie’s conception in the context of contemporary school experience and the impact on learning to teach. Challenging the acceptance of Lortie’s view to pre-service teachers’ prior experience and the impact on learning to teach has significance for all teacher education programs.
... While there has been much positive attention on growth mindset as a tool to improve learning, a recent double meta-study of growth mindset has indicated that the effectiveness of interventions aimed at increasing growth mindset may have been overstated (Sisk et al., 2018). The meta-study did however indicate that for academically at-risk students and students from low socioeconomic status (SES) homes, there was a significant effect of intervention. ...
We developed and launched an online, course-integrated module called Growth & Goals aimed to help students better develop evidence-based learning skills. The module focuses on five main concepts: self-regulated learning, goal-setting, metacognition, mindfulness, and mindsets (growth and fixed continuum). Growth & Goals is an open education resource available for download at no cost to any educator through FlynnResearchGroup.com/GrowthGoals. The module is available in both French and English and can be customized to any university course. The module addresses the aforementioned concepts through a combination of text and videos, with interspersed interactive activities that students use to develop their learning skills. Growth & Goals is intended to help students effectively manage the challenges they may encounter as they progress through their postsecondary academic career and beyond and become more proficient learners. Since 2017, the module has been implemented in more than 15 university courses and has been used by over 8000 students. The preliminary evaluation of Growth & Goals has been largely positive, indicating that the module has been well received by both students and educators and that it successfully guides students in learning the module’s concepts.
... In contrast, believing in malleable characteristics of abilities helps to persevere when facing difficulties, motivates to take a risk or to set ambitious goals (Mrazek et al., 2018;Mueller & Dweck, 1998;Sarrasin et al., 2018). In consequence, holding a growth mindset was associated with greater achievements (e.g., Blackwell et al., 2007;Sisk et al., 2018;Yeager & Dweck, 2012). So far, one could view these two mindsets as opposing ends of a continuum-if people are convinced that some abilities are stable, they should be confident that they are unchangeable. ...
We examined how students’ intrinsic and extrinsic academic motivation and parental education uniquely and interactively related to teacher report of their self-regulated classroom behaviors (e.g., completion of tasks, keeping track of instructions). In a socioeconomically and racially/ethnically diverse sample of 317 students in third through fifth grade from the United States (34% Asian/Pacific Islander, 32% Hispanic/Latine, 21% White, 6% Black, 6% multiracial/other; 52% female), neither intrinsic motivation nor extrinsic academic motivation emerged as a significant predictor of children’s self-regulated classroom behaviors when controlling for parental education. However, we found a significant interactive effect between intrinsic motivation and parental education for three complementary measures of students’ self-regulated classroom behaviors (task orientation, working memory, flexible shifting). Simple slope analyses revealed that the positive association between intrinsic motivation and students’ self-regulated classroom behaviors was limited to children whose parents have lower levels of educational attainment (e.g., high school degree). This work has important practice and policy implications for increasing classroom practices that promote students’ academic intrinsic motivation, particularly for students whose parents have a high school degree or less. Simple interventions to improve teachers’ autonomy-supportive classroom practices and the content of verbal and written feedback to students could have cascading benefits for students’ intrinsic motivation and the self-regulated classroom behaviors that support learning.
This study examined the conceptual structure and motivational effects of growth mindsets based on the perspective of character strengths. An internet survey was conducted with 1,000 workers (500 males and 500 females; age range 20–59 years, M = 40.1 years, SD = 10.7 years). Participants were presented with 25 strengths (i.e., intelligence and 24 character strengths) and were asked to rate their perceived competence, growth mindset, and improvement intention for each strength. The exploratory factor analyses on perceived competence, growth mindset, and improvement intention identified five common factors: wisdom, willpower, temperance, transcendence, and groupness. Moreover, regression analyses indicated that a growth mindset was more strongly related to improvement intention than perceived competence. Furthermore, regression analyses found domain‐specific effects of growth mindsets on improvement intention. Thus, the intention to improve a particular strength was more closely related to that particular growth mindset than to other growth mindsets. The theoretical and educational implications are aired in the discussion section.
With the rapidly growing availability of scalable psychological assessments, personality science holds great promise for the scientific study and applied use of customized behavior-change interventions. To facilitate this development, we propose a classification system that divides psychological targeting into two approaches that differ in the process by which interventions are designed: audience-to-content matching or content-to-audience matching. This system is both integrative and generative: It allows us to (a) integrate existing research on personalized interventions from different psychological subdisciplines (e.g., political, educational, organizational, consumer, and clinical and health psychology) and to (b) articulate open questions that generate promising new avenues for future research. Our objective is to infuse personality science into intervention research and encourage cross-disciplinary collaborations within and outside of psychology. To ensure the development of personality-customized interventions aligns with the broader interests of individuals (and society at large), we also address important ethical considerations for the use of psychological targeting (e.g., privacy, self-determination, and equity) and offer concrete guidelines for researchers and practitioners.
Little is known about the learners’ experience of the remediation process. This chapter reviews available literature on learners’ described preferences related to remediation, specific remediation approaches, honesty in their coaches, and a desire for their personal life factors to be considered in the process. Evidence from domains outside remediation and health professions education is explored to illuminate learners’ emotions, motivation, and insight as they may relate to remediation. The impact of institutional or professional culture on remediating learners is also considered. Finally, the next steps to use learners’ experiences to improve remediation, primarily by destigmatizing the remediation process, are offered.KeywordsLearner perspectiveEmotionMetacognitionMotivationInsightCulture
All the preceding chapters have led to this one fundamental question: can intelligence be increased? It is a simple question, but what exactly does it mean? As discussed in Chapters 2 and 3, from a scientific standpoint, intelligence can mean an assessment score (from a reliable and valid standardized test), a broad factor (like verbal, visuospatial, or perceptual ability), and the general factor common to all mental abilities (the g-factor). The measured performance on any given cognitive test results from the contribution of g, the specific ability tapped by the test, and the specific skills required for such a test. Therefore, when we observe an increase in the measures we administer, the change can be at the test, the ability, or the g level. An increase can be small, albeit statistically significant, or large enough to have a measurable effect on a relevant outcome variable like educational achievement or job performance. An increase can be temporary or long-lasting. In this chapter, we mean something potentially more interesting than an increase in IQ scores, something that is more permanent, and something that impacts g. As you were reading other chapters, perhaps you considered questions like the following:
• Is there anything I can do to be more intelligent?
• Can intelligence be increased beyond a person’s genetic potential?
• Is there a theoretical limit on just how smart any individual can become?
• Do children and adults have an inner genius that can be unlocked?
The desire to enhance intelligence dramatically is as ancient as alchemy. So far, this goal is just as elusive as turning lead to gold – but is it even possible that any of these questions can be answered in the affirmative?
In this revised and updated edition of Hunt's classic textbook, Human Intelligence, two research experts explain how key scientific studies have revealed exciting information about what intelligence is, where it comes from, why there are individual differences, and what the prospects are for enhancing it. The topics are chosen based on the weight of evidence, allowing readers to evaluate what ideas and theories the data support. Topics include IQ testing, mental processes, brain imaging, genetics, population differences, sex, aging, and likely prospects for enhancing intelligence based on current scientific evidence. Readers will confront ethical issues raised by research data and learn how scientists pursue answers to basic and socially relevant questions about why intelligence is important in everyday life. Many of the answers will be surprising and stimulate readers to think constructively about their own views.
While most people believe that creativity is malleable, some perceive it as fixed and unchangeable. What is overlooked by current theorizing is how social factors—like culture or media—influence such mindsets. This chapter focuses on how media across cultures present a perspective on the nature of abilities. We examine a specific case of the so-called talent shows, in which a panel of judges assess participants. By thematic analyses conducted across three cultures (China, Poland, and the USA), we explore mindsets presented in “… Got Talent!” programs. Finally, we discuss the opportunities and threats associated with the role of social factors for what we think about our own abilities and opportunities for their development.KeywordsCreative mindsetsFixed mindsetGrowth mindsetMediaThematic analysis
Seventy-five general and 65 special education teachers working in the same 65 elementary schools in 12 different school districts were surveyed about their mindsets concerning the malleability of writing and intelligence as well as their practices for teaching writing. All teachers taught writing to one or more fourth-grade students receiving special education services, including students with learning disabilities. Both general and special education teachers typically held a growth mindset toward the malleability of writing and intelligence. Collectively, these teachers' mindsets predicted writing frequency (i.e., frequency of students' writing) and how often they taught writing skills and processes once variance due to teachers' preparation, efficacy to teach writing, teaching experience, and type of teacher was first controlled. The observed relationships between teachers' mindsets and reported practices for teaching writing were not mediated by type of teacher (i.e., general or special education). General and special education teachers did not differ in writing frequency for three types of writing collectively (narrative, informative, and persuasive) or how frequently they made 18 adaptations for teaching writing collectively, but general education teachers reported teaching writing skills and processes more often than their special education counterparts. Recommendations for future research and implications for practice are presented.
Purpose of the study: The current study explored a possible association between students' beliefs about their intelligence and academic achievement and compared gender differences in terms of these two variables. Methodology: The sample of the study comprised of four hundred and fifty (male and female) MSc mathematics students, randomly selected from seven public sector universities of Khyber Pakhtunkhwa, Pakistan. A scale developed by Dweck (1999) was adapted to collect data for this study. Academic achievement was measured through students' previous examination scores. Findings: Findings of the study showed that male students believed more in 'incremental' intelligence and had significantly higher academic achievement as compared to their female counterparts. A significant relationship was found between students' beliefs in 'incremental' intelligence and their academic achievement. Applications of the study: The study has important implications for teachers and academics in the subjects of science and mathematics. This study also has implications for policies planners and administration in terms of developing an understanding regarding the role of students' beliefs about intelligence and academic achievement. The study could lead to new thinking about ways to work on the beliefs of students that could result in better academic achievement. The novelty of this study: The study could also lead to further studies regarding the role of gender in affecting incremental beliefs and academic achievement.
https://ppk.elte.hu/dstore/document/1216/Z%C3%83%C2%B3lyomi%20Anna_t%C3%83%C2%A9zisf%C3%83%C2%BCzet_magyar.pdf
Growth mindset interventions directed at students aim to change students' beliefs about the malleability of ability. These interventions have had mixed results, with some showing impressive findings (e.g., improving grades and persistence in science and closing performance gaps), while other implementations have shown null findings. This heterogeneity suggests that growth mindset interventions should not be viewed as a sole solution for improving educational outcomes for students and that further research is needed to identify the contextual factors that influence their effectiveness. We propose new theoretical directions in mindset research that adopts an anti‐deficit model and moves away from focusing exclusively on students and their belief systems. Instead, we encourage a new wave of mindset research that considers the institutional, cultural, and contextual environment that either corroborates or negates students' mindset beliefs. We propose a new approach to mindset research that emphasizes innovative approaches to better understand the conditions under which mindset interventions are effective.
Because of overwhelming evidence of publication bias in psychology, techniques to correct meta-analytic estimates for such bias are greatly needed. The methodology on which the p-uniform and p-curve methods are based has great promise for providing accurate meta-analytic estimates in the presence of publication bias. However, in this article, we show that in some situations, p-curve behaves erratically, whereas p-uniform may yield implausible estimates of negative effect size. Moreover, we show that (and explain why) p-curve and p-uniform result in overestimation of effect size under moderate-to-large heterogeneity and may yield unpredictable bias when researchers employ p-hacking. We offer hands-on recommendations on applying and interpreting results of meta-analyses in general and p-uniform and p-curve in particular. Both methods as well as traditional methods are applied to a meta-analysis on the effect of weight on judgments of importance. We offer guidance for applying p-uniform or p-curve using R and a user-friendly web application for applying p-uniform.
It has been claimed that working memory training programs produce diverse beneficial effects. This article presents a meta-analysis of working memory training studies (with a pretest-posttest design and a control group) that have examined transfer to other measures (nonverbal ability, verbal ability, word decoding, reading comprehension, or arithmetic; 87 publications with 145 experimental comparisons). Immediately following training there were reliable improvements on measures of intermediate transfer (verbal and visuospatial working memory). For measures of far transfer (nonverbal ability, verbal ability, word decoding, reading comprehension, arithmetic) there was no convincing evidence of any reliable improvements when working memory training was compared with a treated control condition. Furthermore, mediation analyses indicated that across studies, the degree of improvement on working memory measures was not related to the magnitude of far-transfer effects found. Finally, analysis of publication bias shows that there is no evidential value from the studies of working memory training using treated controls. The authors conclude that working memory training programs appear to produce short-term, specific training effects that do not generalize to measures of “real-world” cognitive skills. These results seriously question the practical and theoretical importance of current computerized working memory programs as methods of training working memory skills.
Background. The p-curve is a plot of the distribution of p-values reported in a set of scientific studies. Comparisons between ranges of p-values have been used to evaluate fields of research in terms of the extent to which studies have genuine evidential value, and the extent to which they suffer from bias in the selection of variables and analyses for publication, p-hacking.
Methods. p-hacking can take various forms. Here we used R code to simulate the use of ghost variables, where an experimenter gathers data on several dependent variables but reports only those with statistically significant effects. We also examined a text-mined dataset used by Head et al. (2015) and assessed its suitability for investigating p-hacking.
Results. We show that when there is ghost p-hacking, the shape of the p-curve depends on whether dependent variables are intercorrelated. For uncorrelated variables, simulated p-hacked data do not give the “p-hacking bump” just below .05 that is regarded as evidence of p-hacking, though there is a negative skew when simulated variables are inter-correlated. The way p-curves vary according to features of underlying data poses problems when automated text mining is used to detect p-values in heterogeneous sets of published papers.
Conclusions. The absence of a bump in the p-curve is not indicative of lack of p-hacking. Furthermore, while studies with evidential value will usually generate a right-skewed p-curve, we cannot treat a right-skewed p-curve as an indicator of the extent of evidential value, unless we have a model specific to the type of p-values entered into the analysis. We conclude that it is not feasible to use the p-curve to estimate the extent of p-hacking and evidential value unless there is considerable control over the type of data entered into the analysis. In particular, p-hacking with ghost variables is likely to be missed.
Systematic reviews and meta-analyses have become increasingly important in health care. Clinicians read them to keep up to date with their field [1],[2], and they are often used as a starting point for developing clinical practice guidelines. Granting agencies may require a systematic review to ensure there is justification for further research [3], and some health care journals are moving in this direction [4]. As with all research, the value of a systematic review depends on what was done, what was found, and the clarity of reporting. As with other publications, the reporting quality of systematic reviews varies, limiting readers' ability to assess the strengths and weaknesses of those reviews.
Several early studies evaluated the quality of review reports. In 1987, Mulrow examined 50 review articles published in four leading medical journals in 1985 and 1986 and found that none met all eight explicit scientific criteria, such as a quality assessment of included studies [5]. In 1987, Sacks and colleagues [6] evaluated the adequacy of reporting of 83 meta-analyses on 23 characteristics in six domains. Reporting was generally poor; between one and 14 characteristics were adequately reported (mean = 7.7; standard deviation = 2.7). A 1996 update of this study found little improvement [7].
In 1996, to address the suboptimal reporting of meta-analyses, an international group developed a guidance called the QUOROM Statement (QUality Of Reporting Of Meta-analyses), which focused on the reporting of meta-analyses of randomized controlled trials [8]. In this article, we summarize a revision of these guidelines, renamed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses), which have been updated to address several conceptual and practical advances in the science of systematic reviews (Box 1).
Box 1: Conceptual Issues in the Evolution from QUOROM to PRISMA
Completing a Systematic Review Is an Iterative Process
The conduct of a systematic review depends heavily on the scope and quality of included studies: thus systematic reviewers may need to modify their original review protocol during its conduct. Any systematic review reporting guideline should recommend that such changes can be reported and explained without suggesting that they are inappropriate. The PRISMA Statement (Items 5, 11, 16, and 23) acknowledges this iterative process. Aside from Cochrane reviews, all of which should have a protocol, only about 10% of systematic reviewers report working from a protocol [22]. Without a protocol that is publicly accessible, it is difficult to judge between appropriate and inappropriate modifications.
BACKGROUND Mental health disorders and self-assessed mental health problems are common among students in tertiary education in Western countries. On average, one in three university students suffer from depressive symptoms and more female students are affected than males. Student mental health services are relevant settings for promoting mental health and preventing mental ill-health through interventions at the organizational-, group-and individual levels. However, student's problems differ across campuses and many intervention programs and policies are not based on the best available evidence. More over, sound interventions may remain without effect unless they are thoroughly implemented.
Many theories about the role of the environment in raising IQ have been put forward. There has not been an equal effort, however, in experimentally testing these theories. In this paper, we test whether the role of the environment in raising IQ is bidirectional/reciprocal. We meta-analyze the evidence for the fadeout effect of IQ, determining whether interventions that raise IQ have sustained effects after they end. We analyze 7584 participants across 39 randomized controlled trials, using a mixed-effects analysis with growth curve modeling. We confirm that after an intervention raises intelligence the effects fade away. We further show this is because children in the experimental group lose their IQ advantage and not because those in the control groups catch up. These findings are inconsistent with a bidirectional/reciprocal model of interaction. We discuss explanations for the fadeout effect and posit a unidirectional-reactive model for the role of the environment in the development of intelligence.
The efficacy of academic-mind-set interventions has been demonstrated by small-scale, proof-of-concept interventions, generally delivered in person in one school at a time. Whether this approach could be a practical way to raise school achievement on a large scale remains unknown. We therefore delivered brief growth-mind-set and sense-of-purpose interventions through online modules to 1,594 students in 13 geographically diverse high schools. Both interventions were intended to help students persist when they experienced academic difficulty; thus, both were predicted to be most beneficial for poorly performing students. This was the case. Among students at risk of dropping out of high school (one third of the sample), each intervention raised students' semester grade point averages in core academic courses and increased the rate at which students performed satisfactorily in core courses by 6.4 percentage points. We discuss implications for the pipeline from theory to practice and for education reform.
© The Author(s) 2015.
Carefully devised and delivered psychological interventions catalyze the effects of high-quality educational reforms, but don't replace them.
Because challenges are ubiquitous, resilience is essential for success in school and in life. In this article we review research demonstrating the impact of students’ mindsets on their resilience in the face of academic and social challenges. We show that students who believe (or are taught) that intellectual abilities are qualities that can be developed (as opposed to qualities that are fixed) tend to show higher achievement across challenging school transitions and greater course completion rates in challenging math courses. New research also shows that believing (or being taught) that social attributes can be developed can lower adolescents’ aggression and stress in response to peer victimization or exclusion, and result in enhanced school performance. We conclude by discussing why psychological interventions that change students’ mindsets are effective and what educators can do to foster these mindsets and create resilience in educational settings.
In Methods of Randomization in Experimental Design, author Valentim R. Alferes presents the main procedures of random assignment and local control in between-subjects experimental designs and the counterbalancing schemes in within-subjects or cross-over experimental designs. Alferes uses a pedagogical strategy that allows the reader to implement all randomization methods by relying on the materials given in the appendices and using common features included in most word processor software.
A companion website at www.sagepub.com/alferes provides downloadable IBM SPSS and R versions of SCRAED, a package that performs simple and complex random assignment in experimental design, including the 18 randomization methods presented in Chapters 2 and 3.
Recent randomized experiments have found that seemingly “small” social-psychological interventions in education—that is, brief exercises that target students’ thoughts, feelings, and beliefs in and about school—can lead to large gains in student achievement and sharply reduce achievement gaps even months and years later. These interventions do not teach students academic content but instead target students’ psychology, such as their beliefs that they have the potential to improve their intelligence or that they belong and are valued in school. When social-psychological interventions have lasting effects, it can seem surprising and even “magical,” leading people either to think of them as quick fixes to complicated problems or to consider them unworthy of serious consideration. The present article discourages both responses. It reviews the theoretical basis of several prominent social-psychological interventions and emphasizes that they have lasting effects because they target students’ subjective experiences in school, because they use persuasive yet stealthy methods for conveying psychological ideas, and because they tap into recursive processes present in educational environments. By understanding psychological interventions as powerful but context-dependent tools, educational researchers will be better equipped to take them to scale. This review concludes by discussing challenges to scaling psychological interventions and how these challenges may be overcome.
In meta-analysis, it is common to have dependent effect sizes, such as several effect sizes from the same sample but measured at different times. Cheung and Chan proposed the adjusted-individual and adjusted-weighted procedures to estimate the degree of dependence and incorporate this estimate in the meta-analysis. The present study extends the previous study by examining the case of heterogeneous degree of dependence. Simulation results reveal that these two procedures again generated less biased estimates of the degree of heterogeneity than the commonly used samplewise procedure and were statistically more powerful to detect true variations. In addition, the adjusted-weighted procedure generated slightly less biased estimates of the degree of heterogeneity than the adjusted-individual weighted procedure across conditions. Future directions to further refine these procedures are discussed.
In many studies included in meta-analyses, the independent variable measure, the dependent variable measure, or both, have been artificially dichotomized, attenuating the correlation from its true value and resulting in (a) a downward distortion in the mean correlation and (b) an upward distortion in the apparent real variation of correlations across studies. We present (a) exact corrections for this distortion for the case in which only one of the variables has been dichotomized and (b) methods for making approximate corrections when both variables have been artificially dichotomized. These approximate corrections are shown to be quite accurate for most research data. Methods for weighting the resulting corrected correlations in meta-analysis are presented. These corrections make it possible for meta-analysis to yield approximately unbiased estimates of mean population correlations and their standard deviations despite the initial distortion in the correlations from individual studies. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Funnel plots, and tests for funnel plot asymmetry, have been widely used to examine bias in the results of meta-analyses. Funnel plot asymmetry should not be equated with publication bias, because it has a number of other possible causes. This article describes how to interpret funnel plot asymmetry, recommends appropriate tests, and explains the implications for choice of meta-analysis modelThe 1997 paper describing the test for funnel plot asymmetry proposed by Egger et al 1 is one of the most cited articles in the history of BMJ.1 Despite the recommendations contained in this and subsequent papers,2 3 funnel plot asymmetry is often, wrongly, equated with publication or other reporting biases. The use and appropriate interpretation of funnel plots and tests for funnel plot asymmetry have been controversial because of questions about statistical validity,4 disputes over appropriate interpretation,3 5 6 and low power of the tests.2This article recommends how to examine and interpret funnel plot asymmetry (also known as small study effects2) in meta-analyses of randomised controlled trials. The recommendations are based on a detailed MEDLINE review of literature published up to 2007 and discussions among methodologists, who extended and adapted guidance previously summarised in the Cochrane Handbook for Systematic Reviews of Interventions.7What is a funnel plot?A funnel plot is a scatter plot of the effect estimates from individual studies against some measure of each study’s size or precision. The standard error of the effect estimate is often chosen as the measure of study size and plotted on the vertical axis8 with a reversed scale that places the larger, most powerful studies towards the top. The effect estimates from smaller studies should scatter more widely at the bottom, with the spread narrowing among larger studies.9 In the absence of bias and between study heterogeneity, the scatter will be due to sampling variation alone and the plot will resemble a symmetrical inverted funnel (fig 1⇓). A triangle centred on a fixed effect summary estimate and extending 1.96 standard errors either side will include about 95% of studies if no bias is present and the fixed effect assumption (that the true treatment effect is the same in each study) is valid. The appendix on bmj.com discusses choice of axis in funnel plots.View larger version:In a new windowDownload as PowerPoint SlideFig 1 Example of symmetrical funnel plot. The outer dashed lines indicate the triangular region within which 95% of studies are expected to lie in the absence of both biases and heterogeneity (fixed effect summary log odds ratio±1.96×standard error of summary log odds ratio). The solid vertical line corresponds to no intervention effectImplications of heterogeneity, reporting bias, and chance Heterogeneity, reporting bias, and chance may all lead to asymmetry or other shapes in funnel plots (box). Funnel plot asymmetry may also be an artefact of the choice of statistics being plotted (see appendix). The presence of any shape in a funnel plot is contingent on the studies having a range of standard errors, since otherwise they would lie on a horizontal line.Box 1: Possible sources of asymmetry in funnel plots (adapted from Egger et al1)Reporting biasesPublication bias: Delayed publication (also known as time lag or pipeline) bias Location biases (eg, language bias, citation bias, multiple publication bias)Selective outcome reportingSelective analysis reportingPoor methodological quality leading to spuriously inflated effects in smaller studiesPoor methodological designInadequate analysisFraudTrue heterogeneitySize of effect differs according to study size (eg, because of differences in the intensity of interventions or in underlying risk between studies of different sizes)ArtefactualIn some circumstances, sampling variation can lead to an association between the intervention effect and its standard errorChanceAsymmetry may occur by chance, which motivates the use of asymmetry testsHeterogeneityStatistical heterogeneity refers to differences between study results beyond those attributable to chance. It may arise because of clinical differences between studies (for example, setting, types of participants, or implementation of the intervention) or methodological differences (such as extent of control over bias). A random effects model is often used to incorporate heterogeneity in meta-analyses. If the heterogeneity fits with the assumptions of this model, a funnel plot will be symmetrical but with additional horizontal scatter. If heterogeneity is large it may overwhelm the sampling error, so that the plot appears cylindrical.Heterogeneity will lead to funnel plot asymmetry if it induces a correlation between study sizes and intervention effects.5 For example, substantial benefit may be seen only in high risk patients, and these may be preferentially included in early, small studies.10 Or the intervention may have been implemented less thoroughly in larger studies, resulting in smaller effect estimates compared with smaller studies.11Figure 2⇓ shows funnel plot asymmetry arising from heterogeneity that is due entirely to there being three distinct subgroups of studies, each with a different intervention effect.12 The separate funnels for each subgroup are symmetrical. Unfortunately, in practice, important sources of heterogeneity are often unknown.View larger version:In a new windowDownload as PowerPoint SlideFig 2 Illustration of funnel plot asymmetry due to heterogeneity, in the form of three distinct subgroups of studies. Funnel plot including all studies (top left) shows clear asymmetry (P<0.001 from Egger test for funnel plot asymmetry). P values for each subgroup are all >0.49.Differences in methodological quality may also cause heterogeneity and lead to funnel plot asymmetry. Smaller studies tend to be conducted and analysed with less methodological rigour than larger studies,13 and trials of lower quality also tend to show larger intervention effects.14 15Reporting biasReporting biases arise when the dissemination of research findings is influenced by the nature and direction of results. Statistically significant “positive” results are more likely to be published, published rapidly, published in English, published more than once, published in high impact journals, and cited by others.16 17 18 19 Data that would lead to negative results may be filtered, manipulated, or presented in such a way that they become positive.14 20 Reporting biases can have three types of consequence for a meta-analysis:A systematic review may fail to locate an eligible study because all information about it is suppressed or hard to find (publication bias) A located study may not provide usable data for the outcome of interest because the study authors did not consider the result sufficiently interesting (selective outcome reporting) A located study may provide biased results for some outcome—for example, by presenting the result with the smallest P value or largest effect estimate after trying several analysis methods (selective analysis reporting).These biases may cause funnel plot asymmetry if statistically significant results suggesting a beneficial effect are more likely to be published than non-significant results. Such asymmetry may be exaggerated if there is a further tendency for smaller studies to be more prone to selective suppression of results than larger studies. This is often assumed to be the case for randomised trials. For instance, it is probably more difficult to make a large study disappear without trace, while a small study can easily be lost in a file drawer.21 The same may apply to specific outcomes—for example, it is difficult not to report on mortality or myocardial infarction if these are outcomes of a large study. Smaller studies have more sampling error in their effect estimates. Thus even though the risk of a false positive significant finding is the same, multiple analyses are more likely to yield a large effect estimate that may seem worth publishing. However, biases may not act this way in real life; funnel plots could be symmetrical even in the presence of publication bias or selective outcome reporting19 22—for example, if the published findings point to effects in different directions but unreported results indicate neither direction. Alternatively, bias may have affected few studies and therefore not cause glaring asymmetry.ChanceThe role of chance is critical for interpretation of funnel plots because most meta-analyses of randomised trials in healthcare contain few studies.2 Investigations of relations across studies in a meta-analysis are seriously prone to false positive findings when there is a small number of studies and heterogeneity across studies,23 and this may affect funnel plot symmetry.Interpreting funnel plot asymmetryAuthors of systematic reviews should distinguish between possible reasons for funnel plot asymmetry (box 1). Knowledge of the intervention, and the circumstances in which it was implemented in different studies, can help identify causes of asymmetry in funnel plots, which should also be interpreted in the context of susceptibility to biases of research in the field of interest. Potential conflicts of interest, whether outcomes and analyses have been standardised, and extent of trial registration may need to be considered. For example, studies of antidepressants generate substantial conflicts of interest because the drugs generate vast sales revenues. Furthermore, there are hundreds of outcome scales, analyses can be very flexible, and trial registration was uncommon until recently.24 Conversely, in a prospective meta-analysis where all data are included and all analyses fully standardised and conducted according to a predetermined protocol, publication or reporting biases cannot exist. Reporting bias is therefore more likely to be a cause of an asymmetric plot in the first situation than in the second.Terrin et al found that researchers were poor at identifying publication bias from funnel plots.5 Including contour lines corresponding to perceived milestones of statistical significance (P=0.01, 0.05, 0.1, etc) may aid visual interpretation.25 If studies seem to be missing in areas of non-significance (fig 3⇓, top) then asymmetry may be due to reporting bias, although other explanations should still be considered. If the supposed missing studies are in areas of higher significance or in a direction likely to be considered desirable to their authors (fig 3⇓, bottom), asymmetry is probably due to factors other than reporting bias. View larger version:In a new windowDownload as PowerPoint SlideFig 3 Contour enhanced funnel plots. In the top diagram there is a suggestion of missing studies in the middle and right of the plot, broadly in the white area of non-significance, making publication bias plausible. In the bottom diagram there is a suggestion of missing studies on the bottom left hand side of the plot. Since most of this area contains regions of high significance, publication bias is unlikely to be the underlying cause of asymmetryStatistical tests for funnel plot asymmetryA test for funnel plot asymmetry (sometimes referred to as a test for small study effects) examines whether the association between estimated intervention effects and a measure of study size is greater than might be expected to occur by chance. These tests typically have low power, so even when a test does not provide evidence of asymmetry, bias cannot be excluded. For outcomes measured on a continuous scale a test based on a weighted linear regression of the effect estimates on their standard errors is straightforward.1 When outcomes are dichotomous and intervention effects are expressed as odds ratios, this corresponds to an inverse variance weighted linear regression of the log odds ratio on its standard error.2 Unfortunately, there are statistical problems because the standard error of the log odds ratio is mathematically linked to the size of the odds ratio, even in the absence of small study effects.2 4 Many authors have therefore proposed alternative tests (see appendix on bmj.com).4 26 27 28Because it is impossible to know the precise mechanism(s) leading to funnel plot asymmetry, simulation studies (in which tests are evaluated on large numbers of computer generated datasets) are required to evaluate test characteristics. Most have examined a range of assumptions about the extent of reporting bias by selectively removing studies from simulated datasets.26 27 28 After reviewing the results of these studies, and based on theoretical considerations, we formulated recommendations on testing for funnel plot asymmetry (box 2). The appendix describes the proposed tests, explains the reasons that some were not recommended, and discusses funnel plots for intervention effects measured as risk ratios, risk differences, and standardised mean differences. Our recommendations imply that tests for funnel plot asymmetry should be used in only a minority of meta-analyses.29Box 2: Recommendations on testing for funnel plot asymmetryAll types of outcomeAs a rule of thumb, tests for funnel plot asymmetry should not be used when there are fewer than 10 studies in the meta-analysis because test power is usually too low to distinguish chance from real asymmetry. (The lower the power of a test, the higher the proportion of “statistically significant” results in which there is in reality no association between study size and intervention effects). In some situations—for example, when there is substantial heterogeneity—the minimum number of studies may be substantially more than 10Test results should be interpreted in the context of visual inspection of funnel plots— for example, are there studies with markedly different intervention effect estimates or studies that are highly influential in the asymmetry test? Even if an asymmetry test is statistically significant, publication bias can probably be excluded if small studies tend to lead to lower estimates of benefit than larger studies or if there are no studies with significant resultsWhen there is evidence of funnel plot asymmetry, publication bias is only one possible explanation (see box 1)As far as possible, testing strategy should be specified in advance: choice of test may depend on the degree of heterogeneity observed. Applying and reporting many tests is discouraged: if more than one test is used, all test results should be reported Tests for funnel plot asymmetry should not be used if the standard errors of the intervention effect estimates are all similar (the studies are of similar sizes)Continuous outcomes with intervention effects measured as mean differencesThe test proposed by Egger et al may be used to test for funnel plot asymmetry.1 There is no reason to prefer more recently proposed tests, although their relative advantages and disadvantages have not been formally examined. General considerations suggest that the power will be greater than for dichotomous outcomes but that use of the test with substantially fewer than 10 studies would be unwiseDichotomous outcomes with intervention effects measured as odds ratiosThe tests proposed by Harbord et al26 and Peters et al27 avoid the mathematical association between the log odds ratio and its standard error when there is a substantial intervention effect while retaining power compared with alternative tests. However, false positive results may still occur if there is substantial between study heterogeneityIf there is substantial between study heterogeneity (the estimated heterogeneity variance of log odds ratios, τ2, is >0.1) only the arcsine test including random effects, proposed by Rücker et al, has been shown to work reasonably well.28 However, it is slightly conservative in the absence of heterogeneity and its interpretation is less familiar than for other tests because it is based on an arcsine transformation.When τ2 is <0.1, one of the tests proposed by Harbord et al,26 Peters et al,27 or Rücker et al28 can be used. Test performance generally deteriorates as τ2 increases.Funnel plots and meta-analysis modelsFixed and random effects modelsFunnel plots can help guide choice of meta-analysis method. Random effects meta-analyses weight studies relatively more equally than fixed effect analyses by incorporating the between study variance into the denominator of each weight. If effect estimates are related to standard errors (funnel plot asymmetry), the random effects estimate will be pulled more towards findings from smaller studies than the fixed effect estimate will be. Random effects models can thus have undesirable consequences and are not always conservative.30The trials of intravenous magnesium after myocardial infarction provide an extreme example of the differences between fixed and random effects analyses that can arise in the presence of funnel plot asymmetry.31 Beneficial effects on mortality, found in a meta-analysis of small studies,32 were subsequently contradicted when the very large ISIS-4 study found no evidence of benefit.33 A contour enhanced funnel plot (fig 4⇓) gives a clear visual impression of asymmetry, which is confirmed by small P values from the Harbord and Peters tests (P<0.001 and P=0.002 respectively).View larger version:In a new windowDownload as PowerPoint SlideFig 4 Contour enhanced funnel plot for trials of the effect of intravenous magnesium on mortality after myocardial infarctionFigure 5⇓ shows that in a fixed effect analysis ISIS-4 receives 90% of the weight, and there is no evidence of a beneficial effect. However, there is clear evidence of between study heterogeneity (P<0.001, I2=68%), and in a random effects analysis the small studies dominate so that intervention appears beneficial. To interpret the accumulated evidence, it is necessary to make a judgment about the validity or relevance of the combined evidence from the smaller studies compared with that from ISIS-4. The contour enhanced funnel plot suggests that publication bias does not completely explain the asymmetry, since many of the beneficial effects reported from smaller studies were not significant. Plausible explanations for these results are that methodological flaws in the smaller studies, or changes in the standard of care (widespread adoption of treatments such as aspirin, heparin, and thrombolysis), led to apparent beneficial effects of magnesium. This belief was reinforced by the subsequent publication of the MAGIC trial, in which magnesium added to these treatments which also found no evidence of benefit on mortality (odds ratio 1.0, 95% confidence interval 0.8 to 1.1).34View larger version:In a new windowDownload as PowerPoint SlideFig 5 Comparison of fixed and random effects meta-analytical estimates of the effect of intravenous magnesium on mortality after myocardial infarctionWe recommend that when review authors are concerned about funnel plot asymmetry in a meta-analysis with evidence of between study heterogeneity, they should compare the fixed and random effects estimates of the intervention effect. If the random effects estimate is more beneficial, authors should consider whether it is plausible that the intervention is more effective in smaller studies. Formal investigations of heterogeneity of effects may reveal explanations for funnel plot asymmetry, in which case presentation of results should focus on these. If larger studies tend to be methodologically superior to smaller studies, or were conducted in circumstances more typical of the use of the intervention in practice, it may be appropriate to include only larger studies in the meta-analysis.Extrapolation of a funnel plot regression lineAn assumed relation between susceptibility to bias and study size can be exploited by extrapolating within a funnel plot. When funnel plot asymmetry is due to bias rather than substantive heterogeneity, it is usually assumed that results from larger studies are more believable than those from smaller studies because they are less susceptible to methodological flaws or reporting biases. Extrapolating a regression line on a funnel plot to minimum bias (maximum sample size) produces a meta-analytical estimate that can be regarded as corrected for such biases.35 36 37 However, because it is difficult to distinguish between asymmetry due to bias and asymmetry due to heterogeneity or chance, the broad applicability of such approaches is uncertain. Further approaches to adjusting for publication bias are described and discussed in the appendix.DiscussionReporting biases are one of a number of possible explanations for the associations between study size and effect size that are displayed in asymmetric funnel plots. Examining and testing for funnel plot asymmetry, when appropriate, is an important means of addressing bias in meta-analyses, but the multiple causes of asymmetry and limited power of asymmetry tests mean that other ways to address reporting biases are also of importance. Searches of online trial registries can identify unpublished trials, although they do not currently guarantee access to trial protocols and results. When there are no registered but unpublished trials, and the outcome of interest is reported by all trials, restricting meta-analyses to registered trials should preclude publication bias. Recent comparisons of results of published trials with those submitted for regulatory approval have also provided clear evidence of reporting bias.38 39 Methods for dealing with selective reporting of outcomes have been described elsewhere. 40Our recommendations apply to meta-analyses of randomised trials, and their applicability in other contexts such as meta-analyses of epidemiological or diagnostic test studies is unclear.41 The performance of tests for funnel plot asymmetry in these contexts is likely to differ from that in meta-analyses of randomised trials. Further factors, such as confounding and precision of measurements, may cause a relation between study size and effect estimates in observational studies. For example, large studies based on routinely collected data might not fully control confounding compared with smaller, purpose designed studies that collected a wide range of potential confounding variables. Alternatively, larger studies might use self reported exposure levels, which are more error prone, while smaller studies used precise measuring instruments. However, simulation studies have usually not considered such situations. An exception is for diagnostic studies, where large imbalances in group sizes and substantial odds ratios lead to poor performance of some tests: that proposed by Deeks et al was designed for use in this context.4Summary points Inferences on the presence of bias or heterogeneity should consider different causes of funnel plot asymmetry and should not be based on visual inspection of funnel plots aloneThey should be informed by contextual factors, including the plausibility of publication bias as an explanation for the asymmetryTesting for funnel plot asymmetry should follow the recommendations detailed in this articleThe fixed and random effects estimates of the intervention effect should be compared when funnel plot asymmetry exists in a meta-analysis with between study heterogeneityNotesCite this as: BMJ 2011;342:d4002FootnotesContributors: All authors contributed to the drafting and editing of the manuscript. DA, JC, JD, RMH, JPTH, JPAI, DRJ, DM, JP, GR, JACS, AJS and JT contributed to the chapter in the Cochrane Handbook for Systematic Reviews of Interventions on which our recommendations on testing for funnel plot asymmetry are based. JACS will act as guarantor.Funding: Funded in part by the Cochrane Collaboration Bias Methods Group, which receives infrastructure funding as part of a commitment by the Canadian Institutes of Health Research (CIHR) and the Canadian Agency for Drugs and Technologies in Health (CADTH) to fund Canadian based Cochrane entities. This supports dissemination activities, web hosting, travel, training, workshops and a full time coordinator position. JPTH was funded by MRC Grant U.1052.00.011. DGA is supported by Cancer Research UK. GR was supported by a grant from Deutsche Forschungsgemeinschaft (FOR 534 Schw 821/2-2).Competing interests. JC, JJD, SD, RMH, JPAI, DRJ, PM, JP, GR, GS, JACS and AJS are all authors on papers proposing tests for funnel plot asymmetry, but have no commercial interests in the use of these tests. All authors have completed the ICJME unified disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare that they have no financial or non-financial interests that may be relevant to the submitted work.Provenance and peer review: Not commissioned; externally peer reviewed.References↵Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ1997;315:629-34.OpenUrlFREE Full Text↵Sterne JAC, Gavaghan D, Egger M. Publication and related bias in meta-analysis: power of statistical tests and prevalence in the literature. J Clin Epidemiol2000;53:1119-29.OpenUrlCrossRefMedlineWeb of Science↵Lau J, Ioannidis JP, Terrin N, Schmid CH, Olkin I. The case of the misleading funnel plot. 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This review builds on self-control theory (Carver & Scheier, 1998) to develop a theoretical framework for investigating associations of implicit theories with self-regulation. This framework conceptualizes self-regulation in terms of 3 crucial processes: goal setting, goal operating, and goal monitoring. In this meta-analysis, we included articles that reported a quantifiable assessment of implicit theories and at least 1 self-regulatory process or outcome. With a random effects approach used, meta-analytic results (total unique N = 28,217; k = 113) across diverse achievement domains (68% academic) and populations (age range = 5-42; 10 different nationalities; 58% from United States; 44% female) demonstrated that implicit theories predict distinct self-regulatory processes, which, in turn, predict goal achievement. Incremental theories, which, in contrast to entity theories, are characterized by the belief that human attributes are malleable rather than fixed, significantly predicted goal setting (performance goals, r = -.151; learning goals, r = .187), goal operating (helpless-oriented strategies, r = -.238; mastery-oriented strategies, r = .227), and goal monitoring (negative emotions, r = -.233; expectations, r = .157). The effects for goal setting and goal operating were stronger in the presence (vs. absence) of ego threats such as failure feedback. Discussion emphasizes how the present theoretical analysis merges an implicit theory perspective with self-control theory to advance scholarship and unlock major new directions for basic and applied research. (PsycINFO Database Record (c) 2012 APA, all rights reserved).
Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Design: Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30
THE STUDY OF SOCIOECONOMIC STATUS (SES) AND THE BRAIN FINDS ITSELF IN A CIRCUMSTANCE UNUSUAL FOR COGNITIVE NEUROSCIENCE: large numbers of questions with both practical and scientific importance exist, but they are currently under-researched and ripe for investigation. This review aims to highlight these questions, to outline their potential significance, and to suggest routes by which they might be approached. Although remarkably few neural studies have been carried out so far, there exists a large literature of previous behavioural work. This behavioural research provides an invaluable guide for future neuroimaging work, but also poses an important challenge for it: how can we ensure that the neural data contributes predictive or diagnostic power over and above what can be derived from behaviour alone? We discuss some of the open mechanistic questions which Cognitive Neuroscience may have the power to illuminate, spanning areas including language, numerical cognition, stress, memory, and social influences on learning. These questions have obvious practical and societal significance, but they also bear directly on a set of longstanding questions in basic science: what are the environmental and neural factors which affect the acquisition and retention of declarative and nondeclarative skills? Perhaps the best opportunity for practical and theoretical interests to converge is in the study of interventions. Many interventions aimed at improving the cognitive development of low SES children are currently underway, but almost all are operating without either input from, or study by, the Cognitive Neuroscience community. Given that longitudinal intervention studies are very hard to set up, but can, with proper designs, be ideal tests of causal mechanisms, this area promises exciting opportunities for future research.
Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses.
Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews.
Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision.
In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias.
A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution.
In the present article, a commonly used meta-analytic procedure for handling dependent effect sizes from a single sample was examined, and 2 revised procedures that estimate and incorporate the degree of interdependence were proposed. The authors' simulation results reveal that the commonly used procedure that averages the effect sizes from a single sample (denoted as the samplewise procedure) underestimates the degree of heterogeneity. The proposed variations are less biased than the samplewise procedure in estimating the degree of heterogeneity in most of the situations that we examined. Future directions to further improve the procedures for handling dependent effect sizes from a single sample are discussed.
Two studies explored the role of implicit theories of intelligence in adolescents' mathematics achievement. In Study 1 with 373 7th graders, the belief that intelligence is malleable (incremental theory) predicted an upward trajectory in grades over the two years of junior high school, while a belief that intelligence is fixed (entity theory) predicted a flat trajectory. A mediational model including learning goals, positive beliefs about effort, and causal attributions and strategies was tested. In Study 2, an intervention teaching an incremental theory to 7th graders (N=48) promoted positive change in classroom motivation, compared with a control group (N=43). Simultaneously, students in the control group displayed a continuing downward trajectory in grades, while this decline was reversed for students in the experimental group.
: Multilevel models have become popular for the analysis of a variety of problems, going beyond the classical individuals-within-groups applications. This chapter gives a brief summary of the reasons for using multilevel models, and provides examples why these reasons are indeed valid reasons. Next, recent (simulation) research is reviewed on the robustness and power of the usual estimation procedures with varying sample sizes. 1 Why multilevel data need multilevel models Multilevel models are models specifically geared toward the statistical analysis of data that have a hierarchical or clustered structure. Such data arise routinely in various fields, for instance in educational research, where pupils are nested within schools, family studies with children nested within families, medical research with patients nested within physicians or hospitals, and biological research, for instance the analysis of dental anomalies with teeth nested within different persons' mouths. Clustered data...
Journals tend to publish only statistically significant evidence, creating a scientific record that markedly overstates the size of effects. We provide a new tool that corrects for this bias without requiring access to nonsignificant results. It capitalizes on the fact that the distribution of significant p values, p-curve, is a function of the true underlying effect. Researchers armed only with sample sizes and test results of the published findings can correct for publication bias. We validate the technique with simulations and by reanalyzing data from the Many-Labs Replication project. We demonstrate that p-curve can arrive at conclusions opposite that of existing tools by reanalyzing the meta-analysis of the “choice overload” literature.
In a well-known article, Carney, Cuddy, and Yap (2010) documented the benefits of “power posing”. In their study, participants (N=42) who were randomly assigned to briefly adopt expansive, powerful postures sought more risk, had higher testosterone levels, and had lower cortisol levels than those assigned to adopt contractive, powerless postures. In their response to a failed replication by Ranehill et al. (2015), Carney, Cuddy, and Yap (2015) reviewed 33 successful studies investigating the effects of expansive vs. contractive posing, focusing on differences between these studies and the failed replication, to identify possible moderators that future studies could explore. But before spending valuable resources on that, it is useful to establish whether the literature that Carney et al. (2015) cited actually suggests that power posing is effective. In this paper we rely on p-curve analysis to answer the following question: Does the literature reviewed by Carney et al. (2015) suggest the existence of an effect once we account for selective reporting? We conclude not. The distribution of p-values from those 33 studies is indistinguishable from what is expected if (1) the average effect size were zero, and (2) selective reporting (of studies and/or analyses) were solely responsible for the significant effects that are published. Although more highly powered future research may find replicable evidence for the purported benefits of power posing (or unexpected detriments), the existing evidence is too weak to justify a search for moderators or to advocate for people to engage in power posing to better their lives.
Significance
This study is the first, to our knowledge, to show that a growth mindset (the belief that intelligence is not fixed and can be developed) reliably predicts achievement across a national sample of students, including virtually all of the schools and socioeconomic strata in Chile. It also explores the relationship between income and mindset for the first time, to our knowledge, finding that students from lower-income families were less likely to hold a growth mindset than their wealthier peers but that those who did hold a growth mindset were appreciably buffered against the deleterious effects of poverty on achievement. These results suggest that mindsets may be one mechanism through which economic disadvantage can affect achievement.
Believing in people's ability to change can help thwart teen depression, spur workplace creativity and ease political conflict
The United States must improve its students' educational achievement. Race, gender, and social class gaps persist, and, overall, U.S. students rank poorly among peers globally. Scientific research shows that students' psychology-their "academic mindsets"-have a critical role in educational achievement. Yet policymakers have not taken full advantage of cost-effective and well-validated mindset interventions. In this article, we present two key academic mindsets. The first, a growth mindset, refers to the belief that intelligence can be developed over time. The second, a belonging mindset, refers to the belief that people like you belong in your school or in a given academic field. Extensive research shows that fostering these mindsets can improve students' motivation; raise grades; and reduce racial, gender, and social class gaps. Of course, mindsets are not a panacea, but with proper implementation they can be an excellent point of entry. We show how policy at all levels (federal, state, and local) can leverage mindsets to lift the nation's educational outcomes.
Journals tend to publish only statistically significant evidence, creating a scientific record that markedly overstates the size of effects. We provide a new tool that corrects for this bias without requiring access to nonsignificant results. It capitalizes on the fact that the distribution of significant p values, p-curve, is a function of the true underlying effect. Researchers armed only with sample sizes and test results of the published findings can correct for publication bias. We validate the technique with simulations and by reanalyzing data from the Many-Labs Replication project. We demonstrate that p-curve can arrive at conclusions opposite that of existing tools by reanalyzing the meta-analysis of the "choice overload" literature.
© The Author(s) 2014.
Discussion in this paper centers around the circumstances under which unequal sample size in the two-group study arises and when the point-biserial correlation resulting therefrom should be construed as attenuated and therefore corrected. Formulas are provided for correcting the coefficient either directly or through the mediation of relevant statistics found in studies where the coefficient itself is not reported. Problems and solutions discussed in the two-group study with a single dependent or independent measure are generalized to the two-group study with two or more such measures.
Adolescents face many academic and emotional challenges in middle school, but notable differences are evident in how well they adapt. What predicts adolescents' academic and emotional outcomes during this period? One important factor might be adolescents' implicit theories about whether intelligence and emotions can change. The current study examines how these theories affect academic and emotional outcomes. One hundred fifteen students completed surveys throughout middle school, and their grades and course selections were obtained from school records. Students who believed that intelligence could be developed earned higher grades and were more likely to move to advanced math courses over time. Students who believed that emotions could be controlled reported fewer depressive symptoms and, if they began middle school with lower well-being, were more likely to feel better over time. These findings illustrate the power of adolescents' implicit theories, suggesting exciting new pathways for intervention. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
Research on implicit theories suggests that incremental beliefs—that attributes are malleable—can help buffer people against the adverse effects of setbacks on goal achievement. We conducted a longitudinal experiment to examine whether an incremental beliefs intervention could help dieters manage their body weight in the face of severe dieting setbacks. To explore the efficacy of our incremental beliefs intervention, we randomly assigned individuals to a control, a knowledge, or an incremental beliefs condition. In addition to examining the main effect of intervention condition on weight-loss across a 12-week period, we also tested the hypothesis that although participants assigned to the control or knowledge intervention condition would gain more weight as dieting setbacks became more severe, participants assigned to the incremental beliefs condition would not. Results supported this hypothesis: Incremental beliefs protected against setback-related weight-gain. Implications for integrating implicit beliefs interventions with obesity relapse prevention programs are discussed.
Because scientists tend to report only studies (publication bias) or analyses (p-hacking) that "work," readers must ask, "Are these effects true, or do they merely reflect selective reporting?" We introduce p-curve as a way to answer this question. P-curve is the distribution of statistically significant p values for a set of studies (ps < .05). Because only true effects are expected to generate right-skewed p-curves-containing more low (.01s) than high (.04s) significant p values-only right-skewed p-curves are diagnostic of evidential value. By telling us whether we can rule out selective reporting as the sole explanation for a set of findings, p-curve offers a solution to the age-old inferential problems caused by file-drawers of failed studies and analyses. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
In this target article, we present evidence for a new model of individual differences in judgments and reactions. The model holds that people's implicit theories about human attributes structure the way they understand and react to human actions and outcomes. We review research showing that when people believe that attributes (such as intelligence or moral character) are fixed, trait-like entities (an entity theory), they tend to understand outcomes and actions in terms of these fixed traits (''I failed the test because I am dumb'' or ''He stole the bread because he is dishonest''). In contrast, when people believe that attributes are more dynamic, malleable, and developable (an incremental theory), they tend refocus less on broad traits and, instead, tend to understand outcomes and actions in terms of more specific behavioral or psychological mediators (''I failed the test because of my effort or strategy'' or ''He stole the bread because he was desperate''). The two frameworks also appear to foster different reactions: helpless versus mastery-oriented responses to personal setbacks and an emphasis on retribution versus education or rehabilitation for transgressions. These findings are discussed in terms of their implications for personality, motivation, and social perception.