Alexander Robitzsch
Biographic Data
| ID | 1177217 |
|---|---|
| NAME | Alexander Robitzsch |
| GIVEN NAMES | Alexander |
| FAMILY NAME | Robitzsch |
| SIGNATURE | ROBITZSCH A |
| AFFILIATIONS | Leibniz Institute for Science and Mathematics Education |
| ORCID | 0000-0002-8226-3132 |
| VERIFIED | Yes |
| TOTAL WORKS | 32 |
| TOTAL CITATIONS | 9 |
| AUTHOR COUNT | 32 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2007 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Estimating the reliability of round‐robin judgments with social relations confirmatory factor analyses
The social relations model (SRM) is commonly used in psychological research to analyse interdependent data from round‐robin designs, where all members of a group rate each other. Based on the recently suggested social relations confirmatory factor analysis (SR‐CFA), we present general formulas for determining the reliability of composites of round‐robin judgments and also derive simpler variants when specific restrictions are applied to the param…
Synthetic data as a method for increasing reproducibility and transparency in educational research
Open data are often regarded as an important step towards improving the reproducibility and transparency of educational science. Yet, data sharing remains rare, and without open data, statistical analyses often remain irreproducible. In this article, we provide an introduction to synthetic data, a statistical technique based on multiple imputation (MI) that can be used to create simulated copies of the data that can be shared even when the origin…
Evaluating a Bayesian Approach for Estimating Moderator Effects in Parameter-Based Meta-Analytic Structural Equation Modeling
Meta-analytic structural equation modeling (MASEM) enables meta-analytic investigations of multivariate models. A common research objective in meta-analyses is identifying study-level moderators that explain heterogeneity across primary studies. Several MASEM approaches have been extended to include moderators; however, research evaluating and comparing these approaches remains scarce. The present study discusses several parameter-based moderated…
Estimating Trends With Differential Item Functioning
In longitudinal assessments, tests are frequently used to estimate trends over time. However, when item parameters lack invariance, time-point comparisons can be distorted, necessitating appropriate statistical methods to achieve accurate estimation. This study compares trend estimates using the two-parameter logistic (2PL) model under item parameter drift (IPD) across five trend-estimation approaches for two time points: First, concurrent calibr…
A Note on the Occurrence of the Illusory Between-Person Component in the Random Intercept Cross-Lagged Panel Model
The random intercept cross-lagged panel model (RICLPM) decomposes longitudinal associations between two processes X and Y into stable between-person associations and temporal within-person changes. In a recent study, Bailey et al. demonstrated through a simulation study that the between-person variance components in the RICLPM can occur only due to the presence of time-varying covariate processes that are omitted from the analysis model. Therefor…
Fitting Single- and Multiple-Indicator STARTS Models as Dynamic Structural Equation Models
Früher war alles besser? Mathematikleistungen von Abiturientinnen und Abiturienten von 1964 und 1996 im Vergleich
Gemäß der Wahrnehmung insbesondere von Hochschullehrenden verringern sich die Fähigkeiten der Abiturientinnen und Abiturienten im Fach Mathematik seit Jahrzehnten beständig. Allerdings liegen bisher kaum empirische Untersuchungen zur Trendentwicklung der Mathematikleistungen in der gymnasialen Oberstufe vor. Um der Frage nachzugehen, ob sich die vermutete negative Trendentwicklung empirisch nachweisen lässt, wurden die Mathematikleistungen von Ab…
Why Full, Partial, or Approximate Measurement Invariance Are Not a Prerequisite for Meaningful and Valid Group Comparisons
It is frequently stated in the literature that measurement invariance is a prerequisite for the comparison of group means or standard deviations of the latent variable in factor models. This article argues that measurement invariance is not necessary for meaningful and valid comparisons across groups. There is unavoidable ambiguity in how researchers can define comparisons if measurement invariance is violated. Moreover, there is no support for p…
A Bayesian Approach to Estimating Reciprocal Effects with the Bivariate STARTS Model
The bivariate Stable Trait, AutoRegressive Trait, and State (STARTS) model provides a general approach for estimating reciprocal effects between constructs over time. However, previous research has shown that this model is difficult to estimate using the maximum likelihood (ML) method (e.g., nonconvergence). In this article, we introduce a Bayesian approach for estimating the bivariate STARTS model and implement it in the software Stan. We discus…
To Check or Not to Check? A Comment on the Contemporary Psychometrics (ConPsy) Checklist for the Analysis of Questionnaire Items
In a recent paper, the first version of the contemporary psychometrics (ConPsy) checklist for assessing measurement tool quality has been published. This checklist aims to provide guidelines and references to researchers to assess measurement properties for newly developed measurement instruments. The ConPsy checklist recommends appropriate statistical methods for measurement instrument evaluation to guide researchers in instrument development an…
A Comparison of Different Approaches for Estimating Cross-Lagged Effects from a Causal Inference Perspective
This article compares different approaches for estimating cross-lagged effects with a cross-lagged panel design under a causal inference perspective. We distinguish between models that rely on no unmeasured confounding (i.e., observed covariates are sufficient to remove confounding) and latent variable-type models (e.g., random intercept cross-lagged panel model) that use parametric assumptions to adjust for unmeasured time-invariant confounding …
Causal Inference with Multilevel Data
Propensity score methods are a widely recommended approach to adjust for confounding and to recover treatment effects with non-experimental, single-level data. This article reviews propensity score weighting estimators for multilevel data in which individuals (level 1) are nested in clusters (level 2) and nonrandomly assigned to either a treatment or control condition at level 1. We address the choice of a weighting strategy (inverse probability …
Exploring the Multiverse of Analytical Decisions in Scaling Educational Large-Scale Assessment Data
In educational large-scale assessment (LSA) studies such as PISA, item response theory (IRT) scaling models summarize students’ performance on cognitive test items across countries. This article investigates the impact of different factors in model specifications for the PISA 2018 mathematics study. The diverse options of the model specification also firm under the labels multiverse analysis or specification curve analysis in the social sciences.…
On the Performance of Bayesian Approaches in Small Samples
This journal recently published a systematic review of simulation studies on the performance of Bayesian approaches for estimating latent variable models in small samples. The authors of this review highlighted that Bayesian approaches can perform poorly (i.e., by exhibiting bias) when the prior distributions are not thoughtfully constructed on the basis of previous knowledge. In this comment, we question whether the bias is the most important cr…
On the Treatment of Missing Item Responses in Educational Large-Scale Assessment Data
Missing item responses are prevalent in educational large-scale assessment studies such as the programme for international student assessment (PISA). The current operational practice scores missing item responses as wrong, but several psychometricians have advocated for a model-based treatment based on latent ignorability assumption. In this approach, item responses and response indicators are jointly modeled conditional on a latent ability and a…
Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables
The analysis of factor structures is one of the most critical psychometric applications. Frequently, variables (i.e., items or indicators) resulting from questionnaires using ordinal items with 2 to 7 categories are used. There are plenty of articles that recommend treating ordinal variables in a factor analysis by default as ordinal and not as continuous imposing a multivariate normal distribution assumption. In this article, we critically refle…
Disentangling different sources of stability and change in students’ academic self-concepts
Academic self-concept (ASC) is characterized by the dual nature of stability and change. That is, students strive for consistency in their self-concept but also receive achievement feedback that leads to changes in ASC. Only a few previous studies have scrutinized the stability of ASC. The STARTS model (Stable, AutoRegressive Trait, and State) disentangles three sources of variation that underlie individual differences in a construct across time:…
Analysis of Interactions and Nonlinear Effects with Missing Data
When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x2) or are included in inte…
Does personality become more stable with age? Disentangling state and trait effects for the big five across the life span using local structural equation modeling
6,012), we estimated latent retest and trait-state-occasion models in a local structural-equation-modeling framework and tested for moderating effects of age on model-specific stability components. There were 3 main findings. First, the retest correlations indicated that inverted U-shaped patterns manifested only in part. Second, for all Big Five characteristics (except conscientiousness in Study 1), the stable trait variance was larger than the …
Multiple Imputation of Missing Data for Multilevel Models
Multiple imputation (MI) is one of the principled methods for dealing with missing data. In addition, multilevel models have become a standard tool for analyzing the nested data structures that result when lower level units (e.g., employees) are nested within higher level collectives (e.g., work groups). When applying MI to multilevel data, it is important that the imputation model takes the multilevel structure into account. In the present paper…
Integrating Covariates into Social Relations Models
The Social Relations Model (SRM) is a conceptual and analytical approach to examining dyadic behaviors and interpersonal perceptions within groups. In an SRM, the perceiver effect describes a person's tendency to perceive other group members in a certain way, whereas the target effect measures the tendency to be perceived by others in certain ways. In SRM research, it is often of interest to relate these individual SRM effects to covariates. Howe…
Self‐esteem development in the school context
OBJECTIVE: When considering that social inclusion is a basic human need, it makes sense that self-esteem is fueled by social feedback and the sense of being liked by others. This is particularly true with respect to early adolescence, when peers become increasingly important. In the current article, we tested which components of social inclusion are particularly beneficial for the development of self-esteem by differentiating between intrapersona…
Exploring Factor Model Parameters across Continuous Variables with Local Structural Equation Models
Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, abili…
Multiple Imputation of Multilevel Missing Data
The treatment of missing data can be difficult in multilevel research because state-of-the-art procedures such as multiple imputation (MI) may require advanced statistical knowledge or a high degree of familiarity with certain statistical software. In the missing data literature, pan has been recommended for MI of multilevel data. In this article, we provide an introduction to MI of multilevel missing data using the R package pan, and we discuss …
A Bayesian Approach to More Stable Estimates of Group-Level Effects in Contextual Studies
Multilevel analyses are often used to estimate the effects of group-level constructs. However, when using aggregated individual data (e.g., student ratings) to assess a group-level construct (e.g., classroom climate), the observed group mean might not provide a reliable measure of the unobserved latent group mean. In the present article, we propose a Bayesian approach that can be used to estimate a multilevel latent covariate model, which correct…
Multiple Imputation of Multilevel Missing Data
The treatment of missing data can be difficult in multilevel research because state-of-the-art procedures such as multiple imputation (MI) may require advanced statistical knowledge or a high degree of familiarity with certain statistical software. In the missing data literature, pan has been recommended for MI of multilevel data. In this article, we provide an introduction to MI of multilevel missing data using the R package pan, and we discuss …
Does personality become more stable with age? Disentangling state and trait effects for the big five across the life span using local structural equation modeling
6,012), we estimated latent retest and trait-state-occasion models in a local structural-equation-modeling framework and tested for moderating effects of age on model-specific stability components. There were 3 main findings. First, the retest correlations indicated that inverted U-shaped patterns manifested only in part. Second, for all Big Five characteristics (except conscientiousness in Study 1), the stable trait variance was larger than the …
Umgang mit fehlenden Werten in der psychologischen Forschung
Fehlende Werte stellen in der empirisch-psychologischen Forschung oftmals ein Problem dar. Häufig verwendete Verfahren wie fallweiser und paarweiser Ausschluss, Regression- und Mean-Imputation sind aus methodischer Sicht defizitär. Alternative Verfahren für die Analyse von Datensätzen mit fehlenden Werten, die in den letzten drei Jahrzehnten entwickelt wurden, werden in der Forschungspraxis noch selten angewendet. Der vorliegende Beitrag führt zu…
The multilevel latent covariate model
In multilevel modeling (MLM), group-level (L2) characteristics are often measured by aggregating individual-level (L1) characteristics within each group so as to assess contextual effects (e.g., group-average effects of socioeconomic status, achievement, climate). Most previous applications have used a multilevel manifest covariate (MMC) approach, in which the observed (manifest) group mean is assumed to be perfectly reliable. This article demons…
Assessing the impact of learning environments
Exploratory Structural Equation Modeling, Integrating CFA and EFA
This study is a methodological-substantive synergy, demonstrating the power and flexibility of exploratory structural equation modeling (ESEM) methods that integrate confirmatory and exploratory factor analyses (CFA and EFA), as applied to substantively important questions based on multidimentional students' evaluations of university teaching (SETs). For these data, there is a well established ESEM structure but typical CFA models do not fit the …
Impact of Missing Data on the Detection of Differential Item Functioning
This article describes the results of a simulation study to investigate the impact of missing data on the detection of differential item functioning (DIF). Specifically, it investigates how four methods for dealing with missing data (listwise deletion, zero imputation, two-way imputation, response function imputation) interact with two methods of DIF detection (Mantel-Haenszel statistic, logistic regression analysis) under three mechanisms of mis…
Doubly-Latent Models of School Contextual Effects
This article is a methodological-substantive synergy. Methodologically, we demonstrate latent-variable contextual models that integrate structural equation models (with multiple indicators) and multilevel models. These models simultaneously control for and unconfound measurement error due to sampling of items at the individual (L1) and group (L2) levels and sampling error due the sampling of persons in the aggregation of L1 characteristics to for…
ICT‐based dynamic assessment to reveal special education students’ potential in mathematics
This paper reports on a research project on information and communication technology (ICT)‐based dynamic assessment. The project aims to reveal the mathematical potential of students in special education. The focus is on a topic that is generally recognised as rather difficult for weak students: subtraction up to 100 with crossing the ten. The students involved in the project were 8–12 years old. Their mathematical level was one to four years beh…
A Bayesian Approach to More Stable Estimates of Group-Level Effects in Contextual Studies
Multilevel analyses are often used to estimate the effects of group-level constructs. However, when using aggregated individual data (e.g., student ratings) to assess a group-level construct (e.g., classroom climate), the observed group mean might not provide a reliable measure of the unobserved latent group mean. In the present article, we propose a Bayesian approach that can be used to estimate a multilevel latent covariate model, which correct…
Exploring Factor Model Parameters across Continuous Variables with Local Structural Equation Models
Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, abili…
Multiple Imputation of Multilevel Missing Data
The treatment of missing data can be difficult in multilevel research because state-of-the-art procedures such as multiple imputation (MI) may require advanced statistical knowledge or a high degree of familiarity with certain statistical software. In the missing data literature, pan has been recommended for MI of multilevel data. In this article, we provide an introduction to MI of multilevel missing data using the R package pan, and we discuss …
Multiple Imputation of Missing Data for Multilevel Models
Multiple imputation (MI) is one of the principled methods for dealing with missing data. In addition, multilevel models have become a standard tool for analyzing the nested data structures that result when lower level units (e.g., employees) are nested within higher level collectives (e.g., work groups). When applying MI to multilevel data, it is important that the imputation model takes the multilevel structure into account. In the present paper…
Integrating Covariates into Social Relations Models
The Social Relations Model (SRM) is a conceptual and analytical approach to examining dyadic behaviors and interpersonal perceptions within groups. In an SRM, the perceiver effect describes a person's tendency to perceive other group members in a certain way, whereas the target effect measures the tendency to be perceived by others in certain ways. In SRM research, it is often of interest to relate these individual SRM effects to covariates. Howe…
Self‐esteem development in the school context
OBJECTIVE: When considering that social inclusion is a basic human need, it makes sense that self-esteem is fueled by social feedback and the sense of being liked by others. This is particularly true with respect to early adolescence, when peers become increasingly important. In the current article, we tested which components of social inclusion are particularly beneficial for the development of self-esteem by differentiating between intrapersona…
Does personality become more stable with age? Disentangling state and trait effects for the big five across the life span using local structural equation modeling
6,012), we estimated latent retest and trait-state-occasion models in a local structural-equation-modeling framework and tested for moderating effects of age on model-specific stability components. There were 3 main findings. First, the retest correlations indicated that inverted U-shaped patterns manifested only in part. Second, for all Big Five characteristics (except conscientiousness in Study 1), the stable trait variance was larger than the …
Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables
The analysis of factor structures is one of the most critical psychometric applications. Frequently, variables (i.e., items or indicators) resulting from questionnaires using ordinal items with 2 to 7 categories are used. There are plenty of articles that recommend treating ordinal variables in a factor analysis by default as ordinal and not as continuous imposing a multivariate normal distribution assumption. In this article, we critically refle…
Disentangling different sources of stability and change in students’ academic self-concepts
Academic self-concept (ASC) is characterized by the dual nature of stability and change. That is, students strive for consistency in their self-concept but also receive achievement feedback that leads to changes in ASC. Only a few previous studies have scrutinized the stability of ASC. The STARTS model (Stable, AutoRegressive Trait, and State) disentangles three sources of variation that underlie individual differences in a construct across time:…
Analysis of Interactions and Nonlinear Effects with Missing Data
When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x2) or are included in inte…
On the Performance of Bayesian Approaches in Small Samples
This journal recently published a systematic review of simulation studies on the performance of Bayesian approaches for estimating latent variable models in small samples. The authors of this review highlighted that Bayesian approaches can perform poorly (i.e., by exhibiting bias) when the prior distributions are not thoughtfully constructed on the basis of previous knowledge. In this comment, we question whether the bias is the most important cr…
On the Treatment of Missing Item Responses in Educational Large-Scale Assessment Data
Missing item responses are prevalent in educational large-scale assessment studies such as the programme for international student assessment (PISA). The current operational practice scores missing item responses as wrong, but several psychometricians have advocated for a model-based treatment based on latent ignorability assumption. In this approach, item responses and response indicators are jointly modeled conditional on a latent ability and a…
A Comparison of Different Approaches for Estimating Cross-Lagged Effects from a Causal Inference Perspective
This article compares different approaches for estimating cross-lagged effects with a cross-lagged panel design under a causal inference perspective. We distinguish between models that rely on no unmeasured confounding (i.e., observed covariates are sufficient to remove confounding) and latent variable-type models (e.g., random intercept cross-lagged panel model) that use parametric assumptions to adjust for unmeasured time-invariant confounding …
Causal Inference with Multilevel Data
Propensity score methods are a widely recommended approach to adjust for confounding and to recover treatment effects with non-experimental, single-level data. This article reviews propensity score weighting estimators for multilevel data in which individuals (level 1) are nested in clusters (level 2) and nonrandomly assigned to either a treatment or control condition at level 1. We address the choice of a weighting strategy (inverse probability …
Exploring the Multiverse of Analytical Decisions in Scaling Educational Large-Scale Assessment Data
In educational large-scale assessment (LSA) studies such as PISA, item response theory (IRT) scaling models summarize students’ performance on cognitive test items across countries. This article investigates the impact of different factors in model specifications for the PISA 2018 mathematics study. The diverse options of the model specification also firm under the labels multiverse analysis or specification curve analysis in the social sciences.…
Früher war alles besser? Mathematikleistungen von Abiturientinnen und Abiturienten von 1964 und 1996 im Vergleich
Gemäß der Wahrnehmung insbesondere von Hochschullehrenden verringern sich die Fähigkeiten der Abiturientinnen und Abiturienten im Fach Mathematik seit Jahrzehnten beständig. Allerdings liegen bisher kaum empirische Untersuchungen zur Trendentwicklung der Mathematikleistungen in der gymnasialen Oberstufe vor. Um der Frage nachzugehen, ob sich die vermutete negative Trendentwicklung empirisch nachweisen lässt, wurden die Mathematikleistungen von Ab…
Why Full, Partial, or Approximate Measurement Invariance Are Not a Prerequisite for Meaningful and Valid Group Comparisons
It is frequently stated in the literature that measurement invariance is a prerequisite for the comparison of group means or standard deviations of the latent variable in factor models. This article argues that measurement invariance is not necessary for meaningful and valid comparisons across groups. There is unavoidable ambiguity in how researchers can define comparisons if measurement invariance is violated. Moreover, there is no support for p…
A Bayesian Approach to Estimating Reciprocal Effects with the Bivariate STARTS Model
The bivariate Stable Trait, AutoRegressive Trait, and State (STARTS) model provides a general approach for estimating reciprocal effects between constructs over time. However, previous research has shown that this model is difficult to estimate using the maximum likelihood (ML) method (e.g., nonconvergence). In this article, we introduce a Bayesian approach for estimating the bivariate STARTS model and implement it in the software Stan. We discus…
Mathematics (23 works) · Statistics (21 works) · Computer Science (20 works) · Econometrics (19 works) · Psychometric Methodologies and Testing (15 works) · Psychology (11 works) · Statistical Methods and Bayesian Inference (8 works) · Bayesian probability (7 works) · Multilevel model (7 works) · Structural equation modeling (7 works)