Inquiry-based instruction is widely regarded as a core feature of high-quality science teaching. This study provides the first empirical examination of its implementation in Austrian primary science classrooms. It aims to (i) assess the extent to which inquiry-based instructional strategies are enacted, (ii) identify distinct instructional profiles among teachers, and (iii) explore associations between these profiles and teacher characteristics, including professionalisation and gender. This study draws on secondary data from the 2019 TIMSS teacher survey in Austria (nstudents = 4,966; nteacher = 339). Latent Class Analysis was employed to identify underlying patterns of instructional practice based on teachers' self-reports. The analysis revealed four distinct instructional profiles that differ significantly in their didactic design and instructional objectives. While approximately two-thirds of teachers reported combining inquiry-based strategies in various ways, only one profile reflects a comprehensive inquiry-oriented approach in which students are actively engaged throughout the inquiry cycle. The remaining profiles are characterised by differences in instructional goals and pedagogical beliefs. Moreover, multinomial logistic regression analyses revealed statistically significant associations between profile membership and teacher characteristics, particularly gender and engagement in professional development. The results underscore the need to strengthen deep instructional structures and reflective competencies in both initial teacher education and ongoing professional development.

Special techniques must be considered during analysis of large-scale educational assessment (LSA) data. In this regard, many software packages are available to support researchers conducting secondary analyses. However, the software packages available for multilevel analyses are somewhat limited and usually contain only a few of the required techniques. In this article, we review the technical details of LSA studies and describe our comparison of software for multilevel analyses by questioning the extent to which these packages take these technical details into account. In accordance with our findings from this comparison, we developed a SAS macro for multilevel analyses of LSA data that meets all technical requirements. The macro SURVEYHLM fits multilevel models with LSA datasets. SURVEYHLM can handle up to three levels. It can fit different correlation structures for the random components and use plausible values as response variables, and the responses do not necessarily need to be normally distributed. Weights can be specified on levels 1, 2 and 3. Scaling of the level-specific weights is possible, and standard errors can be based on a sandwich estimator or calculated with either the jackknife replication technique or through user-supplied replication weights. Examples of applications are given.

Teachers constantly need to make decisions about which instructional strategies to apply in their classrooms, in particular in the context of so-called adaptive teaching. Research, therefore, needs to explore how such decisions are informed and chosen. Using teacher and student data from the Austrian 2019 TIMS study, the present cross-sectional empirical study addressed three issues. First, we explored the frequency of use of inquiry-based instructional strategies (IQIS) by Austrian primary school science teachers. Second, we asked whether there was a potential relationship between this frequency of use and science teachers’ perceived challenges in terms of heterogeneity, achievement and motivation? Finally, we examined potential associations between the frequency of use of specific IQIS and science teachers’ characteristics. Using an IRT-approach and multiple regressions we managed to identify the four inquiry-based instructional strategies: Demonstrating, Connecting, Involving and Cognitively Activating. Our results show a significant association between teachers’ perceptions, teachers’ characteristics and the frequency of use of IQIS. This suggests that primary school science teachers’ perceptions of their class composition directly influence their choice of IQIS and that this perception is co-determined by certain teacher characteristics. The results are discussed in relation to educational equity, implications for pre-service training, and further training for primary school teachers but also regarding inquiry-based instruction, as our results contrast with numerous studies that examine student ratings as a basis for instructional quality.

In this article, we extend Liao’s test for across-group comparisons of the fixed effects from the generalized linear model to the fixed and random effects of the generalized linear mixed model (GLMM). Using as our basis the Wald statistic, we developed an asymptotic test statistic for across-group comparisons of these effects. The test can be applied when the fixed and random effects are multivariate normally distributed, and it works well for any link function and conditional distribution of the dependent variable of the GLMM. We also derived the asymptotic properties of this test, and because power information does not exist for either our new test statistic or Liao’s test, we implemented a power study to demonstrate the superiority of these tests over the alternatively proposed F test. Using an example, we show the application of the test and then discuss its possible restrictions with respect to the distribution of the random effects.

The cyber insurance market has expanded dramatically over the last decade; however, the underlying risk is still poorly understood by both cyber insurers and academic researchers. Although the quantification of cyber damage is a mainstay in the literature and of ongoing interest for researchers, several research questions requiring at least a somewhat accurate stochastic model for industry or countrywide cyber damages, are mostly disregarded by the academic community or only discussed in qualitative terms. Thus, this paper proposes a macroeconomic framework for generating plausible damage estimates based on current cyber insurance rate schedules, along with a nested hierarchical copula model for implementing the correlation between cyber damages. The resulting sample datasets will be made available to researchers to help spur an additional quantitative push in the literature.

Munster ; New York : Waxmann 2020, 365 S. Padagogische Teildisziplin: Empirische Bildungsforschung; Fachdidaktik/mathematisch-naturwissenschaftliche Facher;

Bullying victimization has been shown to negatively impact academic achievement. However, under certain circumstances, levels of academic achievement might also be a cause of bullying victimization. Previous research has shown that at least in Western countries, high school engagement is connoted by students as un-masculine. Therefore, high school engagement and achievement in school violate boys', but not girls', peer-group norm. This might put high-achieving boys at higher risk of bullying victimization as compared to high-achieving girls. The present study investigated boys' and girls' risk of bullying victimization, depending on different achievement levels. To this end, representative data of N = 3928 German fourth grade students were analyzed. Results showed that boys among the top-performers and also boys among the worst performers had a markedly higher risk of being bullied than girls showing the same achievement, whereas there were no such risk differences between genders in the average achievement groups. The relation between academic achievement and bullying victimization, features with regard to gender, and directions for future research are discussed.

Bibliographie: Schurig, Michael/Kasper, Daniel: Zur Interpretation von Messfehlern aus Sicht der Erziehungswissenschaft, Erziehungswissenschaft, 1-2018, S. 46-53. https://doi.org/10.3224/ezw.v29i1.06

The present study investigated (a) how a latent profile analysis based on representative data of N = 74,868 4th graders from 17 European countries would cluster the students on the basis of their reading, mathematics, and science achievement test scores; (b) whether there would be gender differences at various competency levels, especially among the top performers; (c) and whether societal gender equity might account for possible cross-national variation in the gender ratios among the top performers. The latent profile analysis revealed an international model with 7 profiles. Across these profiles, the test scores of all achievement domains progressively and consistently increased. Thus, consistent with our expectations, (a) the profiles differed only in their individuals' overall performance level across all academic competencies and not in their individuals' performance profile shape. From the national samples, the vast majority of the students could be reliably assigned to 1 of the profiles of the international model. Inspection of the gender ratios revealed (b) that boys were overrepresented at both ends of the competency spectrum. However, there was (c) some cross-national variation in the gender ratios among the top performers, which could be partly explained by women's access to education and labor market participation. The interrelatedness of academic competencies and its practical implications, the role of gender equity as a possible cause of gender differences among the top performers, and directions for future research are discussed.

Large-scale cross-national studies designed to measure student achievement use different social, cultural, economic and other background variables to explain observed differences in that achievement. Prior to their inclusion into a prediction model, these variables are commonly scaled into latent background indices. To allow cross-national comparisons of the latent indices, measurement invariance is assumed. However, it is unclear whether the assumption of measurement invariance has some influence on the results of the prediction model, thus challenging the reliability and validity of cross-national comparisons of predicted results.

Im Jahr 2015 beteiligte sich Deutschland zum dritten Mal an der Grundschuluntersuchung Trends in International Mathematics and Science Study (TIMSS 2015). Mit TIMSS werden alle vier Jahre die Fachleistungen von Schulerinnen und Schulern der vierten Jahrgangsstufe in den Bereichen Mathematik und Naturwissenschaften im internationalen Vergleich untersucht.An TIMSS 2015 waren - neben Deutschland - weltweit 47 Staaten und Regionen als regulare Teilnehmer mit Schulerinnen und Schulern der vierten Jahrgangsstufe beteiligt. In diesem Band werden die Ergebnisse von TIMSS 2015 fur die Bildungsdiskussion in Deutschland erschlossen. Die Ergebnisse des internationalen Vergleichs werden vor dem Hintergrund von Themen dargestellt, die das Lehren und Lernen an Grundschulen in Deutschland verandert und den Bildungsdiskurs der letzten Jahre besonders gepragt haben. Im Fokus stehen neben der Betrachtung von Schulerleistungen in Mathematik und Naturwissenschaften im internationalen Vergleich Leistungsdisparitaten zwischen Jungen und Madchen sowie Kindern unterschiedlicher sozialer und kultureller Herkunft. Daruber hinaus werden zentrale Lehr- und Lernbedingungen in den Blick genommen, wobei Gestaltungsmerkmale des Unterrichts und die Aus- und Fortbildung von Lehrkraften, Lernbedingungen an Ganztagsschulen sowie die Inanspruchnahme von Nachhilfe differenziert betrachtet werden. Daruber hinaus werden der Ubergang von der Grundschule in die Sekundarstufe I und soziale Kompetenzen von Grundschulkindern untersucht.Mit vertiefenden Analysen und der Einordnung der Ergebnisse in den aktuellen Forschungsstand stellt der Band eine differenzierte und anschlussfahige Bestandsaufnahme zur Qualitat mathematischer und naturwissenschaftlicher Bildung in der Grundschule dar und nimmt Entwicklungen seit 2007 in den Blick.Dieser Bericht wendet sich somit an eine Leserschaft, die an bildungspolitischen, padagogischen und fachdidaktischen Fragestellungen interessiert ist.

In Progress in International Reading Literacy Study (PIRLS) educational inequalities are measured, amongst others, through the relationship between students' reading achievements and the home resource for learning (HRL) scale. By applying the partial credit model and using the WLE estimates for the person parameters it is accepted that the distribution of this latent variable is asymptotically normal within participating countries. This assumption is challenged from a theoretical perspective and through empirical findings. To find out how far the distributional properties of the HRL index influence the results of educational inequality measurements, the HRL index is rescaled for 21 European countries who participated in PIRLS 2011, assuming three different prior distributions of the latent index and using the EAP estimates for the person parameters. The predictive effects of these latent indices on students reading achievement were estimated with spline regressions. A positively skewed distribution of the latent index in the marginal maximum likelihood for estimating the item parameters results in the best fit of the scaling model in most countries. In addition, the pattern of signs of the estimated spline coefficients across the knots and the non-linear correlation between the latent index and student reading achievement varies considerably across the prior and empirical distributional properties of the latent index. Thus, by interpreting educational inequalities measured through the relationship between students' reading achievements and the HRL scale in PIRLS, distributional properties of the HRL index should be taken into account.

To understand the relationship between social background and sex in schooling, we use Bourdieu's theory of social reproduction and a feminist perspective of gender as practice. We pose two questions: (1) What is the relationship between economic and cultural capital and achievement for 4th-grade females versus males studying in Germany? (2) Is the relationship between school composition and student achievement different for 4th-grade females versus males? We report no differences between females and males in the relationships between social background and achievement (p > 0.05). However, the relationship between class-aggregated social background and achievement is halved in female-majority mathematics classrooms (beta = -12.6, p < 0.05).

Background In 2011 the Progress in International Reading Literacy Study (PIRLS) and the Trends in International Mathematics and Science Study (TIMSS) were conducted at fourth grade in a number of participating countries with a shared representative sample. In this article we investigate whether there are multidimensional proficiency patterns across the competency domains or not. Methods In order to derive proficiency patterns across the reading (PIRLS), mathematics and science (TIMSS) competence domains, latent profile analyses (LPA) of students’ plausible values were conducted. For this, the grade four student sample from 17 countries were combined and analyzed. The international reference model that resulted from this analysis was then applied with constraints to all 17 countries separately so that substantial comparisons between countries became possible. To describe and compare the differences between national profiles a classification system was developed and applied to all countries’ profile patterns. Results As a result of these international LPA seven groups of learners were identified. The profiles were approximately equidistant and parallel. For all countries we find that achievement across domains can be explained by a general level of achievement rather than subject-specific strengths or weaknesses of learners. However, subject-specific strengths and weaknesses can be identified but are—with the exception of Malta and Northern Ireland—for most of the countries rather small. For only about half of the countries, a rather uniform pattern of subject-specific strengths and weaknesses can be found on all competence levels. The subject itself varies between countries. In the other countries high, intermediate and low achievers differ in their relative subject-specific strength and weaknesses. Conclusions The results suggest that differences in average achievement in TIMSS and PIRLS should also on country level be interpreted with caution. International comparative studies should further investigate potential reasons for the differences between countries.

In psychometric latent variable modeling approaches such as item response theory one of the most central assumptions is local independence (LI), i.e. stochastic independence of test items given a latent ability variable (e.g., Hambleton et al., Fundamentals of item response theory, 1991). This strong assumption, however, is often violated in practice resulting, for instance, in biased parameter estimation. To visualize the local item dependencies, we derive a measure quantifying the degree of such dependence for pairs of items. This measure can be viewed as a dissimilarity function in the sense of psychophysical scaling (Dzhafarov and Colonius, Journal of Mathematical Psychology 51:290–304, 2007), which allows us to represent the local dependencies graphically in the Euclidean 2D space. To avoid problems caused by violation of the local independence assumption, in this paper, we apply a more general concept of “local independence” to psychometric items. Latent class models with random effects (LCMRE; Qu et al., Biometrics 52:797–810, 1996) are used to formulate a generalized local independence (GLI) assumption held more frequently in reality. It includes LI as a special case. We illustrate our approach by investigating the local dependence structures in item types and instances of large scale assessment data from the Programme for International Student Assessment (PISA; OECD, PISA 2009 Technical Report, 2012).

A personal trait, for example a person's cognitive ability, represents a theoretical concept postulated to explain behavior. Interesting constructs are latent, that is, they cannot be observed. Latent variable modeling constitutes a methodology to deal with hypothetical constructs. Constructs are modeled as random variables and become components of a statistical model. As random variables, they possess a probability distribution in the population of reference. In applications, this distribution is typically assumed to be the normal distribution. The normality assumption may be reasonable in many cases, but there are situations where it cannot be justified. For example, this is true for criterion-referenced tests or for background characteristics of students in large scale assessment studies. Nevertheless, the normal procedures in combination with the classical factor analytic methods are frequently pursued, despite the effects of violating this "implicit" assumption are not clear in general. In a simulation study, we investigate whether classical factor analytic approaches can be instrumental in estimating the factorial structure and properties of the population distribution of a latent personal trait from educational test data, when violations of classical assumptions as the aforementioned are present. The results indicate that having a latent non-normal distribution clearly affects the estimation of the distribution of the factor scores and properties thereof. Thus, when the population distribution of a personal trait is assumed to be non-symmetric, we recommend avoiding those factor analytic approaches for estimation of a person's factor score, even though the number of extracted factors and the estimated loading matrix may not be strongly affected. An application to the Progress in International Reading Literacy Study (PIRLS) is given. Comments on possible implications for the Programme for International Student Assessment (PISA) complete the presentation.

The Programme for International Student Assessment (PISA; e.g., OECD, Sample tasks from the PISA 2000 assessment, 2002a; OECD, Learning for tomorrow’s world: first results from PISA 2003, 2004; OECD, PISA 2006: Science competencies for tomorrow’s world, 2007; OECD, PISA 2009 Technical Report, 2012) is an international large scale assessment study that aims to assess the skills and knowledge of 15-year-old students, and based on the results, to compare education systems across the participating (about 70) countries (with a minimum number of approx. 4,500 tested students per country). Initiator of this Programme is the Organisation for Economic Co-operation and Development (OECD; www.pisa.oecd.org ). We review the main methodological techniques of the PISA study. Primarily, we focus on the psychometric procedure applied for scaling items and persons. PISA proficiency scale construction and proficiency levels derived based on discretization of the continua are discussed. For a balanced reflection of the PISA methodology, questions and suggestions on the reproduction of international item parameters, as well as on scoring, classifying and reporting, are raised. We hope that along these lines the PISA analyses can be better understood and evaluated, and if necessary, possibly be improved.