Christian Geiser

Polarization-induced distortions in optical fiber networks with polarization-mode dispersion and polarization-dependent losses

We review the formalism required to investigate the combined effects of polarization-mode dispersion (PMD) and polarization dependent losses (PDL) in optical fiber networks. The combination of PMD and PDL may lead to anomalous dispersion, which is not correctly described by a direct application of the Jones matrix eigenanalysis (JME) method. This calls for a careful assessment of PMD measurement methods in the presence of PDL. We also present a theoretical analysis of distortions in analog transmissions, and computer simulations of digital transmissions. These show that distributed PDL increases the power penalty of the transmission more than lumped PDL at the end of the channel.

Structural equation modeling of multitrait-multimethod data: Different models for different types of methods.

The question as to which structural equation model should be selected when multitrait-multimethod (MTMM) data are analyzed is of interest to many researchers. In the past, attempts to find a well-fitting model have often been data-driven and highly arbitrary. In the present article, the authors argue that the measurement design (type of methods used) should guide the choice of the statistical model to analyze the data. In this respect, the authors distinguish between (a) interchangeable methods, (b) structurally different methods, and (c) the combination of both kinds of methods. The authors present an appropriate model for each type of method. All models allow separating measurement error from trait influences and trait-specific method effects. With respect to interchangeable methods, a multilevel confirmatory factor model is presented. For structurally different methods, the correlated trait-correlated (method-1) model is recommended. Finally, the authors demonstrate how to appropriately analyze data from MTMM designs that simultaneously use interchangeable and structurally different methods. All models are applied to empirical data to illustrate their proper use. Some implications and guidelines for modeling MTMM data are discussed.

Separating "Rotators" From "Nonrotators" in the Mental Rotations Test: A Multigroup Latent Class Analysis

Items of mental rotation tests can not only be solved by mental rotation but also by other solution strategies. A multigroup latent class analysis of 24 items of the Mental Rotations Test (MRT) was conducted in a sample of 1,695 German pupils and students to find out how many solution strategies can be identified for the items of this test. The results showed that five subgroups (latent classes) can be distinguished. Although three of the subgroups differ mainly in the number of items reached, one class shows are very low performance. In another class, a special solution strategy is used. This strategy seems to involve analytic rather than mental rotation processes and is efficient only for a special MRT item type, indicating that not all MRT items require a mental rotation approach. In addition, the multigroup analysis revealed significant sex differences with respect to the class assignment, confirming prior findings that on average male participants perform mental rotation tasks faster and better than female participants. Females were also overrepresented in the analytic strategy class. The results are discussed with respect to psychometric and substantive implications, and suggestions for the optimization of the MRT items are provided.

Changes in intentions, planning, and self-efficacy predict changes in behaviors: An application of latent true change modeling

Can latent true changes in intention, planning, and self-efficacy account for latent change in two health behaviors (physical activity as well as fruit and vegetable intake)? Baseline data on predictors and behaviors and corresponding follow-up data four weeks later were collected from 853 participants. Interindividual differences in change and change—change associations were analyzed using structural equation modeling. For both behaviors, similar prediction patterns were found: changes in intention and self-efficacy predicted changes in planning, which in turn corresponded to changes in behavior. This evidence confirms that change predicts change, which is an inherent precondition in behavior change theories.

Is adding more indicators to a latent class analysis beneficial or detrimental? Results of a Monte-Carlo study

The purpose of this study was to examine in which way adding more indicators or a covariate influences the performance of latent class analysis (LCA). We varied the sample size (100 ≤ N ≤ 2000), number, and quality of binary indicators (between 4 and 12 indicators with conditional response probabilities of [0.3, 0.7], [0.2, 0.8], or [0.1, 0.9]), and the strength of covariate effects (zero, small, medium, large) in a Monte Carlo simulation study of 2- and 3-class models. The results suggested that in general, a larger sample size, more indicators, a higher quality of indicators, and a larger covariate effect lead to more converged and proper replications, as well as fewer boundary parameter estimates and less parameter bias. Furthermore, interactions among these study factors demonstrated how using more or higher quality indicators, as well as larger covariate effect size, could sometimes compensate for small sample size. Including a covariate appeared to be generally beneficial, although the covariate parameters themselves showed relatively large bias. Our results provide useful information for practitioners designing an LCA study in terms of highlighting the factors that lead to better or worse performance of LCA.

A comparison of four approaches to account for method effects in latent state trait analyses

Latent state-trait (LST) analysis is frequently applied in psychological research to determine the degree to which observed scores reflect stable person-specific effects, effects of situations and/or person-situation interactions, and random measurement error. Most LST applications use multiple repeatedly measured observed variables as indicators of latent trait and latent state residual factors. In practice, such indicators often show shared indicator-specific (or method) variance over time. In this article, the authors compare 4 approaches to account for such method effects in LST models and discuss the strengths and weaknesses of each approach based on theoretical considerations, simulations, and applications to actual data sets. The simulation study revealed that the LST model with indicator-specific traits (Eid, 1996) and the LST model with M - 1 correlated method factors (Eid, Schneider, & Schwenkmezger, 1999) performed well, whereas the model with M orthogonal method factors used in the early work of Steyer, Ferring, and Schmitt (1992) and the correlated uniqueness approach (Kenny, 1976) showed limitations under conditions of either low or high method-specificity. Recommendations for the choice of an appropriate model are provided.

Analyzing True Change in Longitudinal Multitrait-Multimethod Studies: Application of a Multimethod Change Model to Depression and Anxiety in Children

The authors show how structural equation modeling can be applied to analyze change in longitudinal multitrait-multimethod (MTMM) studies. For this purpose, an extension of latent difference models (McArdle, 1988; Steyer, Eid, & Schwenkmezger, 1997) to multiple constructs and multiple methods is presented. The model allows investigators to separate true change from measurement error and to analyze change simultaneously for different methods. The authors also show how Campbell and Fiskes (1959) guidelines for analyzing convergent and discriminant validity can be applied to the measurement of latent change. The practical application of the multimethod change model is illustrated in a reanalysis of child depression and anxiety scores (N = 906 American children) that were assessed by self- and parent reports on three measurement occasions. The analyses revealed that (a) the convergent validity of change was low for both constructs and (b) sex was a significant predictor of self-reported, but not of parent reported, anxiety states. Finally, the authors discuss advantages and limitations and compare the model with other approaches for analyzing longitudinal MTMM data.

Cross-Informant Symptoms from CBCL, TRF, and YSR : Trait and Method Variance in a Normative Sample of Russian Youths.

A large community-based sample of Russian youths (n = 841, age M = 13.17 years, SD = 2.51) was assessed with the Child Behavior Checklist (mothers and fathers separately), Teachers Report Form, and Youth Self-Report. The multiple indicator-version of the correlated trait-correlated method minus one, or CT-C(M - 1), model was applied to analyze (a) the convergent and divergent validity of these instruments in Russia, (b) the degree of trait-specificity of rater biases, and (c) potential predictors of rater-specific effects. As expected, based on the published results from different countries and in different languages, the convergent validity of the instruments was rather high between mother and father reports, but rather low for parent, teacher, and self-reports. For self- and teacher reports, rater-specific effects were related to age and gender of the children for some traits. These results, once again, attest to the importance of incorporating information from multiple observers when psychopathological traits are evaluated in children and adolescents.

Analyzing the convergent and discriminant validity of states and traits: Development and applications of multimethod latent state-trait models.

The analysis of convergent and discriminant validity is an integral part of the construct validation process. Models for analyzing the convergent and discriminant validity have typically been developed for cross-sectional data. There exist, however, only a few approaches for longitudinal data that can be applied for analyzing the construct validity of fluctuating states. In this article, the authors show how models of latent state-trait theory can be combined with models of multitrait-multimethod analysis to develop a model that allows for analyzing convergent and discriminant validity in time: the multimethod latent state-trait model. The model allows for identifying different sources of variance (trait consistency, trait-method specificity, occasion-specific consistency, occasion-specific method specificity, and unreliability). It is applied to the repeated measurement of depression and anxiety in children, which was assessed by self and teacher reports (N = 375). The application shows that the proposed models fit the data well and allow a deeper understanding of method effects in clinical assessment.

A Theory of States and Traits—Revised

We present a revision of latent state-trait (LST-R) theory with new definitions of states and traits. This theory applies whenever we study the consistency of behavior, its variability, and its change over time. States and traits are defined in terms of probability theory. This allows for a seamless transition from theory to statistical modeling of empirical data. LST-R theory not only gives insights into the nature of latent variables but it also takes into account four fundamental facts: Observations are fallible, they never happen in a situational vacuum, they are always made using a specific method of observations, and there is no person without a past. Although the first fact necessitates considering measurement error, the second fact requires allowances for situational fluctuations. The third fact implies that, in the first place, states and traits are method specific. Furthermore, compared to the previous version of LST theory (see, e.g., Steyer et al. 1992 , 1999 ), our revision is based on the notion of a person-at-time-t. The new definitions in LST-R theory have far-reaching implications that not only concern the properties of states, traits, and the associated concepts of measurement errors and state residuals, but also are related to the analysis of states and traits in longitudinal observational and intervention studies.