Method effects due to social desirability as a parsimonious explanation of the deviation from unidimensionality in LOT-R scores

Dispositional optimism measured by the Life Orientation Test (Revised; LOT-R, Scheier et al., 1994) was originally proposed as a unidimensional construct pertaining to individual differences in positive outcome expectancies. However, factor analytic research has suggested two separate “optimism” and “pessimism” factors associated with positively and negatively worded items respectively. Using confirmatory factor analysis with data from N = 658 students it is shown that a general optimism model with method effects of the positively worded items fits even better than the “optimism–pessimism” model in terms of the AIC statistic, which accounts for both model parsimony and model fit. Furthermore, an attempt is made to explain the method effects by social desirability: impression management, one of two components of socially desirable responding, correlates significantly with the method factor whereas the other component, self-deceptive enhancement, is related to the general optimism factor. Thus it is shown that the deviation from unidimensionality of observed scores does not imply deviation from unidimensionality of optimism when method effects are incorporated in the model.     

The structure of the relationship between attention and intelligence

The relationship between attention and general intelligence was investigated considering the different types of attention: alertness, sustained attention, focused attention, attentional switching, divided attention, attention according to the supervisory attentional system, attention as inhibition, spatial attention, attention as planning, interference, attention as arousal, and attention according to the assessment tradition. In a sample of 197 participants the relationship of attention and intelligence was investigated by means of structural equation modeling. The results revealed that each type of attention was substantially related to intelligence on the latent level. Furthermore, a high degree of overlap in predicting intelligence was observed for the various types of attention. Comprehensive models based on resources theory and Posner's dimensions were also investigated. The best model of the relationship between attention and intelligence included two first-order and one second-order latent variables of attention and one of intelligence. It predicted 32% of the variance of intelligence.     

A Confirmatory Analysis of Item Reliability Trends (CAIRT): Differentiating True Score and Error Variance in the Analysis of Item Context Effects

Numerous studies have shown increasing item reliabilities as an effect of the item position in personality scales. Traditionally, these context effects are analyzed based on item-total correlations. This approach neglects that trends in item reliabilities can be caused either by an increase in true score variance or by a decrease in error variance. This article presents the Confirmatory Analysis of Item Reliability Trends (CAIRT) that allows estimating both trends separately within a structural equation modeling framework. Results of a simulation study prove the CAIRT method to provide reliable and independent parameter estimates; the power exceeds the analysis of item-total correlations. We present an empirical application to self- and peer ratings collected in an Internet-based experiment. Results show that reliability trends are caused by increasing true score variance in self-ratings and by decreasing error variance in peer ratings.     

Cross-sectional and longitudinal confirmatory factor models for the german test anxiety inventory: A construct validation

Construct validity of the German Test Anxiety Inventory (TAI-G) was tested in two respects. Firstly, the purported four-dimensional structure of the TAI-G (comprising subscales Emotionality, Worry, Interference, and Lack of Confidence) as well as relations of the test anxiety dimensions to self-efficacy were tested. Secondly, the trait conception of the TAI-G was tested within the framework of Latent State-Trait theory. The TAI-G was given to a student sample (N=302) on three occasions with a time interval of 2 weeks along with a study-specific self-efficacy scale on occasion 1. Dimensionality assumptions as well as relations with self-efficacy were tested using cross-sectional second-order confirmatory factor analysis. The trait conception was tested separately for TAI-G subscales by specifying longitudinal confirmatory factor models (Latent State-Trait models) and by calculating variance proportions of manifest variables (Latent State-Trait coefficients) referring to different sources of systematic variance (person, situation, and method) based on parameter estimates of the models. Results were supportive of both the purported four-dimensional structure and hypothesized relationships to self-efficacy (i.e., acceptable model fit) as well as of the trait conception of test anxiety (i.e., acceptable model fit and high proportion of variance due to person component). Implications for further validation studies were discussed.     

Confidence Analyses of Self-Interpretation and Self-Description in Depressive Behaviour

The present paper represents an investigation in the procedure to validate a new questionnaire (Salzburg Subjective Behavioural Analysis, SSBA). This questionnaire is based on a new approach to the diagnosis of depressive behaviour. It is hypothesized that a patient suffering from a depressive disorder loses the ability to produce one or more modes of behaviour at a given time and simultaneously has the urge to produce one or more modes of behaviour constantly. Although the patient is capable of describing this behavioural disorder, he/she is unable to interpret it confidently. This inability of interpretation of depressive behaviour assessed with the SSBA may represent a special kind of cognitive impairment in depression that is tested in conducting an analysis of confidence. The analysis of confidence shows that the capability of interpretation in the patient group is significantly impaired. Finally, a discriminant analysis allows to differentiate between depressive behaviour and normal behaviour based on the questionnaire.     

Maximum likelihood estimation of latent interaction effects with the LMS method

In the context of structural equation modeling, a general interaction model with multiple latent interaction effects is introduced. A stochastic analysis represents the nonnormal distribution of the joint indicator vector as a finite mixture of normal distributions. The Latent Moderated Structural Equations (LMS) approach is a new method developed for the analysis of the general interaction model that utilizes the mixture distribution and provides a ML estimation of model parameters by adapting the EM algorithm. The finite sample properties and the robustness of LMS are discussed. Finally, the applicability of the new method is illustrated by an empirical example. This research has been supported by a grant from the Deutsche Forschungsgemeinschaft, Germany, No. Mo 474/1 and Mo 474/2. The data for the empirical example have been provided by Andreas Thiele of the University of Frankfurt, Germany. The authors are indebted to an associate editor and to three anonymous reviewers ofPsychometrika whose comments and suggestions have been very helpful.     

Determinants of pain experience: Perceived competence, trait anxiety, trait depression and moderator effects

The study examined determining and moderator variables of chronic pain experience. The data indicated strong influences of perceived competence as well as trait anxiety and trait depression. Furthermore some moderator effects could be detected, which were able to explain some extreme states of pain in addition to the effect of the determining variables: subjects with low perceived competence and high trait anxiety suffered significantly more from their pain than subjects with high perceived competence or low trait anxiety. The consequences for further research as well as for pain therapy are discussed.     

On the validity of Raven’s matrices test: Does spatial ability contribute to performance?

The paper reports on an investigation of the convergent and discriminant validity of Raven’s matrices in considering reasoning and spatial abilities. Raven’s advanced progressive matrices (APM) and four scales representing reasoning, visualization, mental rotation and closure were applied to a sample of N = 280 university students. The data were investigated by means of structural equation modeling. The results demonstrated convergent validity of APM with respect to reasoning. In contrast, in investigating discriminant validity substantial correlations of moderate size between APM and the scales representing spatial abilities were observed. However, the investigation of the structure of the prediction of APM revealed that the scales representing spatial abilities did not improve the prediction based on the reasoning scale alone at the latent level. Consequently, it is suggested that Raven’s matrices show good convergent validity and slightly impaired discriminant validity.     

Decomposing person and occasion-specific effects: an extension of latent state-trait (LSI) theory to hierarchical LST models

An extension of latent state-trait (LST) theory to hierarchical LST models is presented. In hierarchical LST models, the covariances between 2 or more latent traits are explained by a general 3rd-order factor, and the covariances between latent state residuals pertaining to different traits measured on the same measurement occasion are explained by 2nd-order latent occasion-specific factors. Analogous to recent developments in multitrait-multimethod methodology, all factors are interpreted in relation to factors taken as comparison standards. An empirical example from test anxiety research illustrates how estimates of additive variance components due to general trait, specific trait, occasion, state residual, method, and measurement error can be obtained using confirmatory factor analysis. Advantages and limitations of these models are discussed.