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When a cognitive ability is assessed repeatedly, test scores and ability estimates are often observed to increase across test sessions. This phenomenon is known as the retest (or practice) effect. One explanation for retest effects is that situational test anxiety interferes with a testee's performance during earlier test sessions, thereby creating systematic measurement bias on the test items (interference hypothesis). Yet, the influence of anxiety diminishes with test repetitions. This explanation is controversial, since the presence of measurement bias during earlier measurement occasions cannot always be confirmed. It is argued that people from the lower end of the ability spectrum become aware of their deficits in test situations and therefore report higher anxiety (deficit hypothesis). In 2014, a structural equation model was proposed that specifically allows the comparison of these two hypotheses with regard to explanatory power for the negative anxiety--ability correlation found in cross-sectional assessments. We extended this model for usage in longitudinal studies to investigate the impact of test anxiety on test performance and on retest effects. A latent neighbor-change growth curve was implemented into the model that enables an estimation of retest effects between all pairs of successive test sessions. Systematic restrictions on model parameters allow testing the hypothetical reduction in anxiety interference over the test sessions, which can be compared to retest effect sizes. In an empirical study with seven measurement occasions, we found that a substantial reduction in interference upon the second test session was associated with the largest retest effect in a figural matrices test, which served as a proxy measure for general intelligence. However, smaller retest effects occurred up to the fourth test administration, whereas evidence for anxiety-induced measurement bias was only produced for the first two test sessions. Anxiety and ability were not negatively correlated at any time when the interference effects were controlled for. Implications, limitations, and suggestions for future research are discussed.

It is shown how the Correlated Traits Correlated Methods Minus One (CTC(M − 1)) Multitrait-Multimethod model for cross-classified data can be modified and applied to divergent thinking (DT)-task responses scored for miscellaneous aspects of creative quality by several raters. In contrast to previous Confirmatory Factor Analysis approaches to analyzing DT-tasks, this model explicitly takes the cross-classified data structure resulting from the employment of raters into account and decomposes the true score variance into target-specific, DT-task object-specific, rater-specific, and rater–target interaction-specific components. This enables the computation of meaningful measurement error-free relative variance-parameters such as trait-consistency, object–method specificity, rater specificity, rater–target interaction specificity, and model-implied intra-class correlations. In the empirical application with alternate uses tasks as DT-measures, the model is estimated using Bayesian statistics. The results are compared to the results yielded with a simplified version of the model, once estimated with Bayesian statistics and once estimated with the maximum likelihood method. The results show high trait-correlations and low consistency across DT-measures which indicates more heterogeneity across the DT-measurement instruments than across different creativity aspects. Substantive deliberations and further modifications, extensions, useful applications, and limitations of the model are discussed.

The present contribution provides a tutorial for the estimation of the social relations model (SRM) by means of structural equation modeling (SEM). In the overarching SEM-framework, the SRM without roles (with interchangeable dyads) is derived as a more restrictive form of the SRM with roles (with noninterchangeable dyads). Starting with the simplest type of the SRM for one latent construct assessed by one manifest round-robin indicator, we show how the model can be extended to multiple constructs each measured by multiple indicators. We illustrate a multiple constructs multiple indicators SEM SRM both with and without roles with simulated data and explain the parameter interpretations. We present how testing the substantial model assumptions can be disentangled from testing the interchangeability of dyads. Additionally, we point out modeling strategies that adhere to cases in which only some members of a group can be differentiated with regards to their roles (i.e., only some group members are noninterchangeable). In the online supplemental materials , we provide concrete examples of specific modeling problems and their implementation into statistical software (Mplus, lavaan, and OpenMx). Advantages, caveats, possible extensions, and limitations in comparison with alternative modeling options are discussed. The present contribution provides a tutorial for the estimation of the social relations model (SRM) by means of structural equation modeling (SEM). In the overarching SEM-framework, the SRM without roles (with interchangeable dyads) is derived as a more restrictive form of the SRM with roles (with noninterchangeable dyads). Starting with the simplest type of the SRM for one latent construct assessed by one manifest round-robin indicator, we show how the model can be extended to multiple constructs each measured by multiple indicators. We illustrate a multiple constructs multiple indicators SEM SRM both with and without roles with simulated data and explain the parameter interpretations. We present how testing the substantial model assumptions can be disentangled from testing the interchangeability of dyads. Additionally, we point out modeling strategies that adhere to cases in which only some members of a group can be differentiated with regards to their roles (i.e., only some group members are noninterchangeable). In the online supplemental materials , we provide concrete examples of specific modeling problems and their implementation into statistical software (Mplus, lavaan, and OpenMx). Advantages, caveats, possible extensions, and limitations in comparison to alternative modeling options are discussed. [ABSTRACT FROM AUTHOR]

It is shown how the Correlated Traits Correlated Methods Minus One (CTC(M − 1)) Multitrait-Multimethod model for cross-classified data can be modified and applied to divergent thinking (DT)-task responses scored for miscellaneous aspects of creative quality by several raters. In contrast to previous Confirmatory Factor Analysis approaches to analyzing DT-tasks, this model explicitly takes the cross-classified data structure resulting from the employment of raters into account and decomposes the true score variance into target-specific, DT-task object-specific, rater-specific, and rater–target interaction-specific components. This enables the computation of meaningful measurement error-free relative variance-parameters such as trait-consistency, object–method specificity, rater specificity, rater–target interaction specificity, and model-implied intra-class correlations. In the empirical application with alternate uses tasks as DT-measures, the model is estimated using Bayesian statistics. The results are compared to the results yielded with a simplified version of the model, once estimated with Bayesian statistics and once estimated with the maximum likelihood method. The results show high trait-correlations and low consistency across DT-measures which indicates more heterogeneity across the DT-measurement instruments than across different creativity aspects. Substantive deliberations and further modifications, extensions, useful applications, and limitations of the model are discussed. [ABSTRACT FROM AUTHOR]

Targeted therapies are a promising alternative to conventional chemotherapy, with an increasing number of therapeutics targeting specific molecular aberrancies in cancer cells. One of the emerging targets for directed cancer treatments is fibroblast growth factor receptors (FGFRs), which are known to be involved in the pathogenesis and progression of multiple cancer types, specially in lung, bladder, and breast cancers. Here, we are demonstrating the development of the FGFR1-targeting agent based on the interactome screening approach, based on the isolation of binding regions from ligands interacting with the receptor. The parallel analysis by FGFR1 pull-down of chymotryptic peptides coupled with MS analysis, and PepSpot analysis yielded equivalent peptide sequences from FGF4, one of the FGFR1 ligands. Three sequences served as a basis for peptibody (Fc-fusion) generation, to overcome clinical limitations of peptidic agents, and two of them showed favorable FGFR1-binding in vitro and FGFR1-dependent internalization into cells. To validate if developed FGFR1-targeting peptibodies can be used for drug delivery, similar to the well-established concept of antibody–drug conjugates (ADCs), peptibodyF4_1 was successfully conjugated with monomethylauristatin E (MMAE), and has shown significant and specific toxicity toward FGFR1-expressing lung cancer cell lines, with nanomolar EC50 values. Essentially, the development of new effective FGFR1 binders that comprise the naturally occurring FGFR-recognition peptides and Fc region ensuring high plasma stability, and long bloodstream circulation is an interesting strategy expanding targeted anticancer agents’ portfolio. Furthermore, identifying peptides effectively binding the receptor from sequences of its ligands is not limited to FGFRs and is an approach versatile enough to be a basis for a new peptide/peptibodies development strategy.

Background: Previous research indicating that women with differences of sexual development (DSD), namely women with Turner syndrome (TS), women with congenital adrenal hyperplasia (CAH), and women with XY-DSD, have an impaired psychosocial and sexual well-being and quality of life (QOL), was often limited by small samples and inadequate control groups (CGs). Only few studies analysed which psychosocial and sexual factors influence QOL in women with DSD and no study so far has examined whether the DSD-condition itself and the diagnostic group to which they belong moderate this influence. Methods: We compared 301 women with TS, 221 women with CAH and 142 women with XY-DSD with 603 non-DSD women regarding depression, anxiety, self-esteem, attention deficit hyperactivity disorder, autism, social participation, body acceptance, relationship status, sexual satisfaction and QOL. Furthermore, we investigated the influence of psychosocial and sexual well-being on QOL within and between diagnostic groups and examined whether the DSD-condition moderates the influence of psychosocial and sexual well-being on QOL. Results: Women with DSD reported average psychosocial well-being and QOL; only women with CAH reported an impaired physical QOL. However, women with DSD were less satisfied with their body and had less often a partner than women in the CG. Women with CAH and XY-DSD were less satisfied with their sex life compared to women in the CG. Across groups, better health and lower depression scores predicted better QOL, whereas higher self-esteem especially predicted better QOL in women with DSD. The presence of DSD moderated the influence of psychosocial and sexual well-being on QOL, however, the specific diagnosis group mainly moderated the influence on physical QOL. Conclusion: We have learned that body and sexual satisfaction need further attention in women with DSD. To optimize their QOL, psychosocial well-being should be taken in account. The improvement of self-esteem seems particularly relevant for women with DSD, as this helps coping with having a variant of sexual development.

AbstractThe social relations model (SRM) is the standard approach for analyzing dyadic data stemming from round-robin designs. The model can be used to estimate correlation-coefficients that reflect the overall reciprocity or accuracy of judgements for individual and dyads on the sample- or population level. Within the social relations structural equation modeling framework and on the statistical grounding of stochastic measurement and classical test theory, we show how the multiple indicator SRM can be modified to capture inter-individual and inter-dyadic differences in reciprocal engagement or inter-individual differences in reciprocal accuracy. All models are illustrated on an open-access round-robin data set containing measures of mimicry, liking, and meta-liking (the belief to be liked). Results suggest that people who engage more strongly in reciprocal mimicry are liked more after an interaction with someone and that overestimating one’s own popularity is strongly associated with being liked less. Further applications, advantages and limitations of the models are discussed. [ABSTRACT FROM AUTHOR]

An automatic item generator for figural memory test items called figumem was developed. It is available in R. A cognitive model allowed the generation of hypothetically parallel items within three difficulty levels determined by visual information load. In a pilot study, participants solved three items for each level of visual load. Within an item response theory approach, the Rasch Poisson counts model and modifications of it were fitted to the data. Results showed overall satisfying fit. Visual information load explained most of the variance in item difficulty. Differences in difficulty between items of the same family were comparatively low, displaying the utility of the item generator for the creation of parallel test forms. Implications, limitations, and suggestions for the use and extensions of figumem are discussed.

We didactically derive a correlated traits correlated (methods – 1) [CTC(M – 1)] multitrait‐multimethod (MTMM) model for dyadic round‐robin data augmented by self‐reports. The model is an extension of the CTC(M – 1) model for cross‐classified data and can handle dependencies between raters and targets by including reciprocity covariance parameters that are inherent in augmented round‐robin designs. It can be specified as a traditional structural equation model. We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M – 1) model for augmented round‐robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round‐robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round‐robin groups are needed to minimize relative parameter estimation inaccuracy. Further, neglecting the reciprocity covariance‐structure of the augmented round‐robin data does not severely bias the remaining parameter estimates. All analyses (including data, R scripts, and results) and the simulation study are provided in the Supporting Information. Implications and limitations are discussed. [ABSTRACT FROM AUTHOR]

When a cognitive ability is assessed repeatedly, test scores and ability estimates are often observed to increase across test sessions. This phenomenon is known as the retest (or practice) effect. One explanation for retest effects is that situational test anxiety interferes with a testee’s performance during earlier test sessions, thereby creating systematic measurement bias on the test items (interference hypothesis). Yet, the influence of anxiety diminishes with test repetitions. This explanation is controversial, since the presence of measurement bias during earlier measurement occasions cannot always be confirmed. It is argued that people from the lower end of the ability spectrum become aware of their deficits in test situations and therefore report higher anxiety (deficit hypothesis). In 2014, a structural equation model was proposed that specifically allows the comparison of these two hypotheses with regard to explanatory power for the negative anxiety−ability correlation found in cross-sectional assessments. We extended this model for usage in longitudinal studies to investigate the impact of test anxiety on test performance and on retest effects. A latent neighbor-change growth curve was implemented into the model that enables an estimation of retest effects between all pairs of successive test sessions. Systematic restrictions on model parameters allow testing the hypothetical reduction in anxiety interference over the test sessions, which can be compared to retest effect sizes. In an empirical study with seven measurement occasions, we found that a substantial reduction in interference upon the second test session was associated with the largest retest effect in a figural matrices test, which served as a proxy measure for general intelligence. However, smaller retest effects occurred up to the fourth test administration, whereas evidence for anxiety-induced measurement bias was only produced for the first two test sessions. Anxiety and ability were not negatively correlated at any time when the interference effects were controlled for. Implications, limitations, and suggestions for future research are discussed.

Introduction: Adolescence is characterized by multiple biopsychosocial changes, associated with a high intraindividual variability of emotional experiences. Previous findings suggest that this intraindividual variability is reflected in a recall bias of adolescents' emotion reports. However, corresponding findings are scarce and inconclusive. Studies on predictors of recall bias in adulthood indicate that personality traits, especially neuroticism and extraversion, as well as specific internalizing disorders might affect recall bias of emotion reports. Methods: The sample consists of 118 Swiss adolescent students in grade 8 and 9 (Mage = 15.15, SDage = 0.89). The students' momentary affective experience was recorded using smartphones over seven consecutive days in situ at 42 randomly generated occasions (six per day), with a total of 1059 protocols on current events. At the end of the experience‐sampling phase, students filled out an online questionnaire, providing information about their personality and typical behavior as well as their retrospective affective experience. In addition, the students' behavior was evaluated by their teachers. We applied two‐level structural equation modeling with latent difference variables. Results: Adolescents high in extraversion showed retrospective overestimation of positive affective experiences and underestimation of negative affective experiences. Adolescents with high neuroticism tended to overestimate negative affect retrospectively, showing no significant effects for positive affect. However, internalizing behavior did not predict a negative recall bias in adolescents' affective experience. Conclusions: Retrospective self‐reports about adolescents' affective experience are biased by relatively stable individual factors, whereas less stable individual factors did not seem to have any influence. [ABSTRACT FROM AUTHOR]

The social relations model is a statistical tool that allows the analysis of group dynamics as dyadic interactions between individuals. Within a multiple group structural equation modeling framework, Wald-tests and likelihood ratio tests based on (1) equality constraints among model parameters and (2) Lagrange multipliers for restrictions among non-linear parameter transformations are presented as methods for group-comparisons of various quantifications of group dynamics that hold different interpretations. The methods are illustrated with an empirical example. A simulation study investigates the performance of the methods with regard to Type I error-rate recoverability and statistical power and displays overall promising results. Implications and limitations are discussed. [ABSTRACT FROM AUTHOR]

Targeted therapies are a promising alternative to conventional chemotherapy, with an increasing number of therapeutics targeting specific molecular aberrancies in cancer cells. One of the emerging targets for directed cancer treatments is fibroblast growth factor receptors (FGFRs), which are known to be involved in the pathogenesis and progression of multiple cancer types, specially in lung, bladder, and breast cancers. Here, we are demonstrating the development of the FGFR1-targeting agent based on the interactome screening approach, based on the isolation of binding regions from ligands interacting with the receptor. The parallel analysis by FGFR1 pull-down of chymotryptic peptides coupled with MS analysis, and PepSpot analysis yielded equivalent peptide sequences from FGF4, one of the FGFR1 ligands. Three sequences served as a basis for peptibody (Fc-fusion) generation, to overcome clinical limitations of peptidic agents, and two of them showed favorable FGFR1-binding in vitro and FGFR1-dependent internalization into cells. To validate if developed FGFR1-targeting peptibodies can be used for drug delivery, similar to the well-established concept of antibody–drug conjugates (ADCs), peptibodyF4_1 was successfully conjugated with monomethylauristatin E (MMAE), and has shown significant and specific toxicity toward FGFR1-expressing lung cancer cell lines, with nanomolar EC50 values. Essentially, the development of new effective FGFR1 binders that comprise the naturally occurring FGFR-recognition peptides and Fc region ensuring high plasma stability, and long bloodstream circulation is an interesting strategy expanding targeted anticancer agents' portfolio. Furthermore, identifying peptides effectively binding the receptor from sequences of its ligands is not limited to FGFRs and is an approach versatile enough to be a basis for a new peptide/peptibodies development strategy. [ABSTRACT FROM AUTHOR]

An automatic item generator for figural memory test items called figumem was developed. It is available in R. A cognitive model allowed the generation of hypothetically parallel items within three difficulty levels determined by visual information load. In a pilot study, participants solved three items for each level of visual load. Within an item response theory approach, the Rasch Poisson counts model and modifications of it were fitted to the data. Results showed overall satisfying fit. Visual information load explained most of the variance in item difficulty. Differences in difficulty between items of the same family were comparatively low, displaying the utility of the item generator for the creation of parallel test forms. Implications, limitations, and suggestions for the use and extensions of figumem are discussed. [ABSTRACT FROM AUTHOR]

Simple Summary: Current approaches to treat cancer include eg. targeted therapies, employing agents, such as antibodies, aimed directly at molecules expressed in cancerous cells. Here we present a targeting molecule alternative to antibodies-peptibodyF2-composed of a peptide responsible for its targeting properties, and an immunoglobulin fragment increasing its stability and improving pharmacokinetic properties. Peptibody F2 specifically binds to fibroblast growth factor receptors (FGFRs) upregulated in multiple types of cancers. Moreover, peptibodyF2 conjugated with the cytotoxic drug (MMAE) serves as an efficient and selective drug carrier delivering cytotoxic payload to cancerous cells expressing FGFR1, leading to their death. Fibroblast growth factor receptors (FGFRs) are emerging targets for directed cancer therapy. Presented here is a new FGFR1-targeting conjugate, the peptibodyF2, which employs peptibody, a fusion of peptide and the Fc fragment of human IgG as a selective targeting agent and drug carrier. Short peptide based on FGF2 sequence was used to construct a FGFR1-targeting peptibody. We have shown that this peptide ensures specific delivery of peptibodyF2 into FGFR1-expressing cells. In order to use peptibodyF2 as a delivery vehicle for cytotoxic drugs, we have conjugated it with MMAE, a drug widely used in antibody–drug conjugates for targeted therapy. Resulting conjugate shows high and specific cytotoxicity towards FGFR1-positive cells, i.e., squamous cell lung carcinoma NCI-H520, while remaining non-toxic for FGFR1-negative cells. Such peptibody–drug conjugate can serve as a basis for development of therapy for tumors with overexpressed or malfunctioning FGFRs. [ABSTRACT FROM AUTHOR]