Lehrer-Schüler-Beziehungen und ihre Korrelate: Ein systematisches Review von Meta-Analysen

Scientific Conference (2023, March 01)

THEORETISCHER HINTERGRUND Gute Beziehungen zur eigenen Lehrerin können sich positiv auf den Erfolg eines Schülers auswirken. Dieser Effekt kann mit Bowlby’s (1982) Bindungstheorie erklärt werden und wird ... [more ▼]

THEORETISCHER HINTERGRUND Gute Beziehungen zur eigenen Lehrerin können sich positiv auf den Erfolg eines Schülers auswirken. Dieser Effekt kann mit Bowlby’s (1982) Bindungstheorie erklärt werden und wird empirisch immer wieder gestützt (z.B. Hamre & Pianta, 2001). Positive Lehrer-Schüler-Beziehungen zeichnen sich durch emotionale Wärme und Nähe aus; negative Aspekte durch Konflikt und Abhängigkeit. So stehen positive Lehrer-Schüler-Beziehungen nicht nur mit akademischen Leistungen in Verbindung, sondern auch mit einer Vielzahl anderer, wünschenswerter Schülerentwicklungen. Zahlreiche Meta-Analysen deuten auf signifikante Zusammenhänge zwischen Lehrer-Schüler-Beziehungen und schulischem Engagement, guten Beziehungen zu Gleichaltrigen, exekutiven Funktionen, allgemeinem Wohlbefinden und der Verringerung aggressiver oder störender Verhaltensweisen hin (Endedijk et al., 2021; Nurmi, 2012; Roorda et al., 2017; Vandenbroucke et al., 2018). Diese Befunde sind jedoch weit verstreut in der Literatur, sodass Forschungslücken unentdeckt bleiben. Auch unterscheiden sich bisherige Überblicksarbeiten in ihren Methoden und den gefundenen Zusammenhängen zwischen Lehrer-Schüler-Beziehungen und Ergebnisvariablen von Schüler*innen. Darüber hinaus ist die Literatur uneindeutig, welche Moderatoren (z.B. Alter oder Geschlecht) diese Beziehungen beeinflussen. Gleichzeitig variiert die Qualität der Meta-Analysen in diesem Feld merklich, was die Interpretation ihrer Ergebnisse erschweren kann. FRAGESTELLUNG Angesichts dieser Forschungslücken haben wir die meta-analytische Literatur systematisch durchsucht und zusammengefasst (Cooper & Koenka, 2012), um einen Überblick über Korrelate von Lehrer-Schüler-Beziehungen zu schaffen. Hierbei untersuchten wir drei Forschungsfragen 1. Inwieweit hängen akademische, verhaltensbezogene, sozio-emotionale, motivationale und kognitive Schülereigenschaften mit Lehrer-Schüler-Beziehungen in der meta-analytischen Literatur zusammen? 2. Welche Moderatoren beeinflussen diese Zusammenhänge? 3. Welche methodische Qualität haben die einbezogenen Meta-Analysen? METHODE Um diese Forschungsfragen zu beantworten, analysierten wir 24 Meta-Analysen, die rund 130 Effektstärken für über eine Million Schüler*innen umfassten. Nach der Präregistrierung erfolgte eine systematische Literatursuche. Während mehrerer Runden der Überprüfung mithilfe unserer Ein- und Ausschlusskriterien identifizierten wir 24 passende Meta-Analysen. Aus diesen Meta-Analysen extrahierten wir die Effektstärken zum Zusammenhang von Lehrer-Schüler-Beziehungen und akademische, verhaltensbezogene, sozio-emotionale, motivationale und allgemeine kognitive Schülereigenschaften. Für die Forschungsfragen 1 und 2 haben wir die Ergebnisse zusammengefasst und einen narrativen Überblick erarbeitet. Für Forschungsfrage 3 bewerteten wir die Qualität der Meta-Analysen mit Hilfe der AMSTAR-2 Skala (angepasst an korrelative Studien in der Psychologie und Bildungsforschung; Shea et al., 2017). ERGEBNISSE UND IHRE BEDEUTUNG Mit Blick auf die Lehrer-Schüler-Beziehungen werden unterschiedliche Ergebnisvariablen analysiert (Forschungsfrage 1). Die stärksten Zusammenhänge zeigten sich für Konflikt und Abhängigkeit in der Lehrer-Schüler-Beziehung mit Verhaltensproblemen der Schüler*innen (r = .35 bis .57; Nurmi, 2012). Positive Lehrer-Schüler-Beziehungen zeigte die stärkste Verbindung mit der Beteiligung in der Schule (r = .26 bis .34; Roorda et al., 2011), prosozialem, externalisierendem und internalisierendem Verhalten (r = .25; Endedijk et al., 2021) sowie mit Lernmotivation in Kombination mit Beteiligung der Schüler*innen (r = .23; Wang et al., 2020). Alter oder Klassenstufe waren die am häufigsten untersuchten Moderatoren mit teilweise gegenläufigen Befunden (Forschungsfrage 2). Geschlechterunterschiede wurden dagegen seltener festgestellt. Gleichzeitig wurde der Effekt der Informationsquelle häufig untersucht, d.h., ob und auf welche Weise Lehrkräfte, Gleichaltrige oder die Schüler*innen selbst die Lehrer-Schüler-Beziehung bewerteten. Für Forschunsgfrage 3 diskutieren wir die Qualitätsunterschiede der Meta-Analysen. Mit dem systematischen Review von Meta-Analysen fassen wir die Forschungslandschaft zu Korrelaten von Lehrer-Schüler-Beziehungen zusammen und zeigen, in welchem Zusammenhang diese mit Lehrer-Schüler-Beziehungen stehen. Unseren Ergebnissen folgend sollten Lehrkräfte für die Wirkung von Lehrer-Schüler-Beziehungen und deren Zusammenhängen sensibilisiert werden. Einige Interventionen zur Verbesserung von dieser wichtigen Beziehungen wurden bereits meta-analytisch mit vielversprechende Ergebnissen untersucht (Kincade et al., 2020). Ein nächster Schritt ist nun die experimentelle Überprüfung der gefundenen Korrelate, um positive Lehrer-Schüler-Beziehungen als wirksame Strategie zur Verbesserung von akademischen, verhaltensbezogenen, sozio-emotionalen, motivationalen und kognitiven Schülereigenschaften kausal zu bestätigen. LITERATUR Bowlby, J. (1982). Attachment and loss: Vol. 1. Attachment. (2nd ed., Vol. 1). Basic Books. Cooper, H., & Koenka, A. C. (2012). The overview of reviews: Unique challenges and opportunities when research syntheses are the principal elements of new integrative scholarship. American Psychologist, 67(6), 446–462. https://doi.org/10.1037/a0027119 Decristan, J., Kunter, M., & Fauth, B. (2022). Die Bedeutung individueller Merkmale und konstruktiver Unterstützung der Lehrkraft für die soziale Integration von Schülerinnen und Schülern im Mathematikunterricht der Sekundarstufe. Zeitschrift für Pädagogische Psychologie, 36(1–2), 85–100. https://doi.org/10.1024/1010-0652/a000329 Endedijk, H. M., Breeman, L. D., van Lissa, C. J., Hendrickx, M. M. H. G., den Boer, L., & Mainhard, T. (2021). The Teacher’s Invisible Hand: A Meta-Analysis of the Relevance of Teacher–Student Relationship Quality for Peer Relationships and the Contribution of Student Behavior. Review of Educational Research, 003465432110514. https://doi.org/10.3102/00346543211051428 Givens Rolland, R. (2012). Synthesizing the Evidence on Classroom Goal Structures in Middle and Secondary Schools: A Meta-Analysis and Narrative Review. Review of Educational Research, 82(4), 396–435. https://doi.org/10.3102/0034654312464909 Hamre, B. K., & Pianta, R. C. (2001). Early Teacher-Child Relationships and the Trajectory of Children’s School Outcomes through Eighth Grade. Child Development, 72(2), 625–638. https://doi.org/10.1111/1467-8624.00301 Kincade, L., Cook, C., & Goerdt, A. (2020). Meta-Analysis and Common Practice Elements of Universal Approaches to Improving Student-Teacher Relationships. Review of Educational Research, 90(5), 710–748. https://doi.org/10.3102/0034654320946836 Korpershoek, H., Harms, T., de Boer, H., van Kuijk, M., & Doolaard, S. (2016). A Meta-Analysis of the Effects of Classroom Management Strategies and Classroom Management Programs on Students’ Academic, Behavioral, Emotional, and Motivational Outcomes. Review of Educational Research, 86(3), 643–680. https://doi.org/10.3102/0034654315626799 Lei, H., Cui, Y., & Chiu, M. M. (2016). Affective Teacher—Student Relationships and Students’ Externalizing Behavior Problems: A Meta-Analysis. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01311 Nurmi, J.-E. (2012). Students’ characteristics and teacher–child relationships in instruction: A meta-analysis. Educational Research Review, 7(3), 177–197. https://doi.org/10.1016/j.edurev.2012.03.001 Roorda, D. L., Jak, S., Zee, M., Oort, F. J., & Koomen, H. M. Y. (2017). Affective Teacher–Student Relationships and Students’ Engagement and Achievement: A Meta-Analytic Update and Test of the Mediating Role of Engagement. School Psychology Review, 46(3), 239–261. https://doi.org/10.17105/SPR-2017-0035.V46-3 Roorda, D. L., Koomen, H. M. Y., Spilt, J. L., & Oort, F. J. (2011). The Influence of Affective Teacher–Student Relationships on Students’ School Engagement and Achievement: A Meta-Analytic Approach. Review of Educational Research, 81(4), 493–529. https://doi.org/10.3102/0034654311421793 Shea, B. J., Reeves, B. C., Wells, G., Thuku, M., Hamel, C., Moran, J., Moher, D., Tugwell, P., Welch, V., Kristjansson, E., & Henry, D. A. (2017). AMSTAR 2: A critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ, j4008. https://doi.org/10.1136/bmj.j4008 Vandenbroucke, L., Spilt, J., Verschueren, K., Piccinin, C., & Baeyens, D. (2018). The Classroom as a Developmental Context for Cognitive Development: A Meta-Analysis on the Importance of Teacher–Student Interactions for Children’s Executive Functions. Review of Educational Research, 88(1), 125–164. https://doi.org/10.3102/0034654317743200 Wang, M.-T., L. Degol, J., Amemiya, J., Parr, A., & Guo, J. (2020). Classroom climate and children’s academic and psychological wellbeing: A systematic review and meta-analysis. Developmental Review, 57, 100912. https://doi.org/10.1016/j.dr.2020.100912 [less ▲]

Investigating Pre-service Teachers’ Attitudes Towards LGB Students in Luxembourg

Report (2022)

This questionnaire study investigates the attitudes of pre-service teachers at the University of Luxembourg towards lesbian, gay and bisexual (LGB) students. Assessing this is crucial when it comes to ... [more ▼]

This questionnaire study investigates the attitudes of pre-service teachers at the University of Luxembourg towards lesbian, gay and bisexual (LGB) students. Assessing this is crucial when it comes to guaranteeing equality in education, as prior research, such as on the Pygmalion effect, demonstrates that teachers’ perceptions of students may have an impact on student’s educational success. This impact can be positive but also negative. Detecting trends of negative attitudes in pre-service teachers may therefore represent a first step in protecting LGB-students from educational inequality. Possible explanatory variables, such as respondents' experiences with LGB people, religiosity, gender or even political orientation will be collected. The aim of the study is to add the aspect of sexual diversity in education to the Luxembourg Education Report 2023, which is entirely devoted to the topic of diversity. We hypothesize that negative attitudes of student teachers towards LGB students are associated with 1) little social contact with LGB people; 2) male gender; 3) stronger religious beliefs; 4) heterosexual orientation; 5) a right-wing, conservative political orientation. Additionally, we examine whether hypergendering (hypermasculinity for men and hyperfemininity for women, i.e., the strict adherence to traditional gender roles and their stereotypes) influences the relationship between negative attitudes regarding LGB students. We hypothesize that 6) men with strong hypermasculinity display more negative attitudes than less hypermasculine men; we tentatively hypothesize that 7) women with strong hyperfemininity display more negative attitudes than less hyperfeminine women towards LGB students. All variables and their concrete assessment tools with respective answer options are displayed in the Survey Overview. [less ▲]

Teacher-Student-Relationships and Student Outcomes in Heterogeneous Educational Settings: A Systematic Review of Meta-Analyses

Scientific Conference (2022, November 09)

Especially in diverse educational settings, positive relationships between students and their teachers can foster students’ learning and help alleviate systematic inequalities. Characterized by emotional ... [more ▼]

Especially in diverse educational settings, positive relationships between students and their teachers can foster students’ learning and help alleviate systematic inequalities. Characterized by emotional warmth or closeness, positive teacher-student relationships (TSR) can improve several student outcomes. For instance, existing meta-analyses suggest significant links between TSR and students’ peer relations, school engagement, academic achievement, emotions, executive functions, general well-being, and reductions in aggressive or disruptive behaviors. However, the evidence on these links is scattered, and a comprehensive overview of the associations with TSR integrating academic, behavioral, socio-emotional, motivational, and general cognitive outcomes is lacking. Furthermore, researchers have been unequivocal about possible moderators, such as how these relationships vary with student age or gender. In light of these research gaps, we systematically reviewed the meta-analytic literature and examined (a) the extent to which academic, behavioral, socio-emotional, motivational, and general cognitive student outcomes are related to TSR in the meta-analytic literature; (b) which moderators influence this association; and (c) the methodological quality of the included meta-analyses. We included meta-analyses with preschool or K-12 samples in our dataset which reported some measure of the relation between TSR and student outcomes. With this dataset, we systematically mapped the evidence on (a) the TSR-outcome relationship; (b) the moderators; and (c) the methodological quality of the meta-analyses. We will present our core findings and discuss future research with this second-order, meta-analytic dataset and the impact of positive TSR in diverse and heterogeneous settings. [less ▲]

The Associations Between Teacher-Student-Relationships and Student Outcomes: A Systematic Review of Meta-Analyses (ReMA-TSR)

Report (2022)

The relationships between students and their teachers can impact students’ learning and development. Characterized by emotional warmth or closeness, positive teacher-student-relationships (TSR) can ... [more ▼]

The relationships between students and their teachers can impact students’ learning and development. Characterized by emotional warmth or closeness, positive teacher-student-relationships (TSR) can improve a variety of student outcomes. Existing meta-analyses suggest strong links between TSR and students’ peer relations, school engagement, academic achievement, emotions, executive functions, general well-being, and reductions in aggressive or disruptive behaviors. However, this evidence base is scattered, and a comprehensive overview of the TSR-outcome associations integrating academic, behavioral, socio-emotional, and general cognitive outcomes is lacking. Further, researchers have been unequivocal about possible moderators, such as how these relationships change with student age as their relationship to family, peers, and teachers change. Considering these research gaps, we aim to systematically review the meta-analytic literature and examine the following two research questions: Research Question 1: To what extent do existing meta-analyses provide evidence supporting significant relations between TSR and children’s academic, behavioral, socioemotional, motivational, and general cognitive outcomes? (Overall relationship) Research Question 2: To what extent do these relationships vary by the characteristics of the meta-analyses, such as student samples, measurement characteristics, and the quality of the meta-analyses? To address these research questions, we conduct a systematic review of existing meta-analyses, integrating the findings of eligible studies. We will include quantitative meta-analyses with preschool or K-12 samples who have no diagnosed disorder or disability. [less ▲]

Are Value-Added Scores Stable Enough for High-Stakes Decisions?

Scientific Conference (2022, March)

Theoretical Background: Can we quantify the effectiveness of a teacher or a school with a single number? Researchers in the field of value-added (VA) models may argue just that (e.g., Chetty et al., 2014 ... [more ▼]

Theoretical Background: Can we quantify the effectiveness of a teacher or a school with a single number? Researchers in the field of value-added (VA) models may argue just that (e.g., Chetty et al., 2014; Kane et al., 2013). VA models are widely used for accountability purposes in education and quantify the value a teacher or a school adds to their students’ achievement. For this purpose, these models predict achievement over time and attempt to control for factors that cannot be influenced by schools or teachers (i.e., sociodemographic & sociocultural background). Following this logic, what is left must be due to teacher or school differences (see, e.g., Braun, 2005). To utilize VA models for high-stakes decision-making (e.g., teachers’ tenure, the allocation of funding), these models would need to be highly stable over time. School-level stability over time, however, has hardly been researched at all and the resulting findings are mixed, with some studies indicating high stability of school VA scores over time (Ferrão, 2012; Thomas et al., 2007) and others reporting a lack of stability (e.g., Gorard et al., 2013; Perry, 2016). Furthermore, as there is no consensus on which variables to use as independent or dependent variables in VA models (Everson, 2017; Levy et al., 2019), the stability of VA could vary between different outcome measures (e.g., language or mathematics). If VA models lack stability over time and across outcome measures, their use as the primary information for high-stakes decision-making is in question, and the inferences drawn from them could be compromised. Questions: With these uncertainties in mind, we examine the stability of school VA model scores over time and investigate the differences between language and mathematics achievement as outcome variables. Additionally, we demonstrate the real-life implications of (in)stable VA scores for single schools and point out an alternative, more constructive use of school VA models in educational research. Method: To study the stability of VA scores on school level over time and across outcomes, we drew on a sample of 146 primary schools, using representative longitudinal data from the standardized achievement tests of the Luxembourg School Monitoring Programme (LUCET, 2021). These schools included a heterogeneous and multilingual sample of 7016 students. To determine the stability of VA scores in the subject of mathematics and in languages over time, we based our analysis on two longitudinal datasets (from 2015 to 2017 and from 2017 to 2019, respectively) and generated two VA scores per dataset, one for language and one for mathematics achievement. We further analyzed how many schools displayed stable VA scores in the respective outcomes over two years, and compared the rank correlations of VA scores between language and mathematics achievement as an outcome variable. Results and Their Significance: Only 34-38 % of the schools showed stable VA scores from grade 1 to 3 with moderate rank correlations of r = .37 with language and r = .34 with mathematics achievement. We therefore discourage using VA models as the only information for high-stakes educational decisions. Nonetheless, we argue that VA models could be employed to find genuinely effective teaching or school practices—especially in heterogeneous student populations, such as Luxembourg, in which educational disparities are an important topic already in primary school (Hoffmann et al., 2018). Consequently, we contrast the school climate and instructional quality, which might be a driver of the differences between schools with stable high vs. low VA scores. Literature Braun, H. (2005). Using student progress to evaluate teachers: A primer on value-added models. Educational Testing Service. Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014). Measuring the impacts of teachers I: Evaluating bias in teacher value-added estimates. American Economic Review, 104(9), 2593–2632. https://doi.org/10.1257/aer.104.9.2593 Everson, K. C. (2017). Value-added modeling and educational accountability: Are we answering the real questions? Review of Educational Research, 87(1), 35–70. https://doi.org/10.3102/0034654316637199 Ferrão, M. E. (2012). On the stability of value added indicators. Quality & Quantity, 46(2), 627–637. https://doi.org/10.1007/s11135-010-9417-6 Gorard, S., Hordosy, R., & Siddiqui, N. (2013). How unstable are “school effects” assessed by a value-added technique? International Education Studies, 6(1), 1–9. https://doi.org/10.5539/ies.v6n1p1 Kane, T. J., McCaffrey, D. F., Miller, T., & Staiger, D. O. (2013). Have We Identified Effective Teachers? Validating Measures of Effective Teaching Using Random Assignment. Research Paper. MET Project. Bill & Melinda Gates Foundation. https://files.eric.ed.gov/fulltext/ED540959.pdf Levy, J., Brunner, M., Keller, U., & Fischbach, A. (2019). Methodological issues in value-added modeling: An international review from 26 countries. Educational Assessment, Evaluation and Accountability, 31(3), 257–287. https://doi.org/10.1007/s11092-019-09303-w LUCET. (2021). Épreuves Standardisées (ÉpStan). https://epstan.lu Perry, T. (2016). English value-added measures: Examining the limitations of school performance measurement. British Educational Research Journal, 42(6), 1056–1080. https://doi.org/10.1002/berj.3247 Thomas, S., Peng, W. J., & Gray, J. (2007). Modelling patterns of improvement over time: Value added trends in English secondary school performance across ten cohorts. Oxford Review of Education, 33(3), 261–295. https://doi.org/10.1080/03054980701366116 [less ▲]

Stability of Primary School Value-Added Scores over Time: A Comparison Between Math and Language Achievement as Outcome Variables

Scientific Conference (2021, November)

Value-added (VA) models are widely used for accountability purposes in education. Tracking a teacher’s or a school’s VA score over time forms oftentimes the basis for high-stakes decision-making and can ... [more ▼]

Value-added (VA) models are widely used for accountability purposes in education. Tracking a teacher’s or a school’s VA score over time forms oftentimes the basis for high-stakes decision-making and can determine whether teachers can keep their jobs or schools may receive certain funding. Despite their high-stakes application, the stability of VA scores over time has not yet been investigated for primary schools. Moreover, it is unclear whether different outcome measures (e.g., language and mathematics) may differ in their stability over time. In the present study, we aimed to clarify the stability of VA scores over time and investigate the differences across outcome variables. Furthermore, we wanted to showcase the real-life implications of (in)stable VA scores for single schools, with a focus on an informative use of VA scores rather than an evaluative way. The exploration of school VA scores in primary schools is especially relevant for heterogeneous student populations, for instance, in Luxembourg. Thus, we drew on representative longitudinal data from the standardized achievement tests of the Luxembourg School Monitoring Programme and examined the stability of school VA scores over two years in 146 schools (N = 7016 students). The overall stability, as measured by correlation coefficients, was moderate with r = .37 for VA scores in language and r = .34 for VA scores in mathematics from grade one to grade three. Real-life implications for schools will be discussed. [less ▲]

Stability of Value-Added Models: Comparing Classical and Machine Learning Approaches

Scientific Conference (2021, September)

Background: What is the value that teachers or schools add to the evolution of students’ performance? Value-added (VA) modeling aims to answer this question by quantifying the effect of pedagogical ... [more ▼]

Background: What is the value that teachers or schools add to the evolution of students’ performance? Value-added (VA) modeling aims to answer this question by quantifying the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds (e.g., Braun, 2005). A plethora of VA models exist, and several outcome measures are in use to estimate VA scores, yet without consensus on the model specification (Everson, 2017; Levy et al., 2019). Furthermore, it is unclear whether the most frequently used VA models (i.e., multi-level, linear regression, and random forest models) and outcome measures (i.e., language and mathematics achievement) indicate a similar stability of VA scores over time. Objectives: Drawing from the data of a highly diverse and multilingual school setting, where leveling out the influence of students’ backgrounds is of special interest, we aim to (a) clarify the stability of school VA scores over time; (b) shed light on the sensitivity toward different statistical models and outcome variables; and (c) evaluate the practical implications of (in)stable VA scores for individual schools. Method: Utilizing the representative, longitudinal data from the Luxembourg School Monitoring Programme (LUCET, 2021), we examined the stability of school VA scores. We drew on two longitudinal data sets of students who participated in the standardized achievement tests in Grade 1 in 2014 or 2016 and then again in Grade 3 two years later (i.e., 2016 and 2018, respectively), with a total of 5875 students in 146 schools. School VA scores were calculated using classical approaches (i.e., linear regression and multilevel models) and one of the most commonly used machine learning approaches in educational research (i.e., random forests). Results and Discussion: The overall stability over time across the VA models was moderate, with multilevel models showing greater stability than linear regression models and random forests. Stability differed across outcome measures and was higher for VA models with language achievement as an outcome variable as compared to those with mathematics achievement. Practical implications for schools and teachers will be discussed. [less ▲]

Linking Executive Functions and Math Intelligence in Preschool Children: A Meta-Analysis

Scientific Conference (2021, May 20)

Background: Executive functions (i.e., response inhibition, attention shifting, working memory updating) have shown to be related to the mathematical component of intelligence, which, in turn, is ... [more ▼]

Background: Executive functions (i.e., response inhibition, attention shifting, working memory updating) have shown to be related to the mathematical component of intelligence, which, in turn, is predictive of various competences later in life. While this relation has already been thoroughly researched in school students and adults, a comprehensive research synthesis on preschool children—a group for which the assessment of these constructs is more challenging—is still missing. Evidence on the differentiation of cognitive skills over time suggests a differential relation of the three executive functions with math intelligence in older but not in younger children. It remains unclear, however, whether and which one of the three executive functions is more closely related to math intelligence in preschool children. Further research gaps concern the measurement of both executive functions and math intelligence in preschool children, as they cannot complete reading- and writing-based questionnaires. Addressing this measurement challenge, a plethora of inventive measurements has been used to assess both cognitive skills. These measurement differences might also have an influence on the relation between executive functions and math intelligence. Objectives: With our meta-analysis, we aimed to clarify the relation between executive functions and math intelligence in preschool children. Further, we wanted to investigate the influence of different measurement methods on this relation and look into the specific links of inhibition, shifting, and updating with math intelligence more closely. Research questions: 1. To what extent are inhibition, shifting, and updating (as a composite and separately) related to math intelligence in preschool children? (Overall correlations) 2. Which sample, study, and measurement characteristics moderate this relation? (Heterogeneity and moderators) 3. How much variation in math intelligence do inhibition, shifting, and updating explain jointly? (Model testing) Methods: We examined the relation between executive functions and math intelligence for 268 effect sizes from 29 studies for a total sample of 25,510 preschool children. Specifically, we synthesized the corresponding correlations by means of three-level random-effects meta-analyses (RQ 1) and examined the study, sample, and measurement characteristics as possible moderators of this relation between EFs and math intelligence via mixed-effects modeling (RQ 2). Further, we performed meta-analytic structural equation modeling to investigate the joint and differential effects inhibition, shifting, and updating on math intelligence (RQ 3). Results: We found executive functions and math intelligence to correlate moderately in preschool children (r = .35). Investigating inhibition, shifting, and updating separately also revealed moderate average correlations to math intelligence (r = .30, r = .38 , and r = .36, respectively). While we did not find age to explain significant amounts of heterogeneity, four measurement characteristics moderated the relation between executive function and math intelligence. When considered jointly through meta-analytic structural equation modeling, the relations of inhibition, shifting, and updating to math intelligence were similar. Conclusions and Implications: By presenting evidence for a significant relation between executive functions and math intelligence also in preschool children, our findings contribute to the discussion on the differentiation of cognitive skills. They highlight the importance of considering measurement characteristics when researching executive functions and math intelligence. Further, we could not confirm that inhibition, shifting, and updating are differentially related to math intelligence. Further research is needed to clarify the impact of age on the relation between executive functions and math intelligence. [less ▲]

The coronavirus (COVID‐19) fatality risk perception of US adult residents in March and April 2020

in British Journal of Health Psychology (2020)

The study compares empirical results on the coronavirus SARS‐CoV‐2 (causing COVID‐19) fatality risk perception of US adult residents stratified for age, gender, and race in mid‐March 2020 (N1 = 1,182) and ... [more ▼]

The study compares empirical results on the coronavirus SARS‐CoV‐2 (causing COVID‐19) fatality risk perception of US adult residents stratified for age, gender, and race in mid‐March 2020 (N1 = 1,182) and mid‐April 2020 (N2 = 953). While the fatality risk perception has increased from March 2020 to April 2020, our findings suggest that many US adult residents severely underestimated their absolute and relative fatality risk (i.e., differentiated for subgroups defined by pre‐existing medical conditions and age) at both time points compared to current epidemiological figures. These results are worrying because risk perception, as our study indicates, relates to actual or intended health‐protective behaviour that can reduce SARS‐CoV‐2 transmission rates. [less ▲]

Implicit motives and children's salivary cortisol reactivity to an adapted version of the Trier Social Stress Test for Children (TSST-C)

in Personality and Individual Differences (2020), 162

This research addresses the interplay between two implicit motives, that are power (nPow) and affiliation (nAff), and the cortisol reactivity (CR) to a psychosocial stressor (an adaption of the Trier ... [more ▼]

This research addresses the interplay between two implicit motives, that are power (nPow) and affiliation (nAff), and the cortisol reactivity (CR) to a psychosocial stressor (an adaption of the Trier Social Stress Test for Children; TSST-C; Buske-Kirschbaum et al., 1997) in 89 healthy children (45 female; Mage = 7.74, SDAge = 0.46). We assessed implicit motives by a 6-image Picture Story Exercise (PSE) and cortisol by 6 saliva samples. As hypothesized, the procedure triggered a significant cortisol reaction, F(1.44, 127.05Greenhouse-Geisser) = 8.22, p = .002, η²part = 0.09. Contrary to our hypothesis, children high in nPow showed no significant increase in CR (β = 0.06, p = .60). However, our results were in line with the findings of Wegner, Schüler, and Budde (2014) that a high implicit affiliation motive is associated with an attenuated CR (β = −0.21, p = .05). Perspectives for future research on implicit motives and children's CR to psychosocial stress are discussed. [less ▲]