Marieke E. Timmerman

Dimensionality Assessment of Ordered Polytomous Items With Parallel Analysis

Parallel analysis (PA) is an often-recommended approach for assessment of the dimensionality of a variable set. PA is known in different variants, which may yield different dimensionality indications. In this article, the authors considered the most appropriate PA procedure to assess the number of common factors underlying ordered polytomously scored variables. They proposed minimum rank factor analysis (MRFA) as an extraction method, rather than the currently applied principal component analysis (PCA) and principal axes factoring. A simulation study, based on data with major and minor factors, showed that all procedures consistently point at the number of major common factors. A polychoric-based PA slightly outperformed a Pearson-based PA, but convergence problems may hamper its empirical application. In empirical practice, PA-MRFA with a 95% threshold based on polychoric correlations or, in case of nonconvergence, Pearson correlations with mean thresholds appear to be a good choice for identification of the number of common factors. PA-MRFA is a common-factor-based method and performed best in the simulation experiment. PA based on PCA with a 95% threshold is second best, as this method showed good performances in the empirically relevant conditions of the simulation experiment.

ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data

MOTIVATION Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such datasets may contain underlying factors, such as time (time-resolved or longitudinal measurements), doses or combinations thereof. Currently used biostatistics methods do not take the structure of such complex datasets into account. However, incorporating this structure into the data analysis is important for understanding the biological information in these datasets. RESULTS We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors.

Three-mode principal components analysis: Choosing the numbers of components and sensitivity to local optima

A method that indicates the numbers of components to use in fitting the three-mode principal components analysis (3MPCA) model is proposed. This method, called DIFFIT, aims to find an optimal balance between the fit of solutions for the 3MPCA model and the numbers of components. The achievement of DIFFIT is compared with that of two other methods, both based on two-way PCAs, by means of a simulation study. It was found that DIFFIT performed considerably better than the other methods in indicating the numbers of components. The 3MPCA model can be estimated by the TUCKALS3 algorithm, which is an alternating least squares algorithm. In a study of how sensitive TUCKALS3 is at hitting local optima, it was found that, if the numbers of components are specified correctly, TUCKALS3 never hits a local optimum. The occurrence of local optima increased as the difference between the numbers of underlying components and the numbers of components as estimated by TUCKALS3 increased. Rationally initiated TUCKALS3 runs hit local optima less often than randomly initiated runs.

The Hull Method for Selecting the Number of Common Factors

A common problem in exploratory factor analysis is how many factors need to be extracted from a particular data set. We propose a new method for selecting the number of major common factors: the Hull method, which aims to find a model with an optimal balance between model fit and number of parameters. We examine the performance of the method in an extensive simulation study in which the simulated data are based on major and minor factors. The study compares the method with four other methods such as parallel analysis and the minimum average partial test, which were selected because they have been proven to perform well and/or they are frequently used in applied research. The Hull method outperformed all four methods at recovering the correct number of major factors. Its usefulness was further illustrated by its assessment of the dimensionality of the Five-Factor Personality Inventory (Hendriks, Hofstee, & De Raad, 1999). This inventory has 100 items, and the typical methods for assessing dimensionality prove to be useless: the large number of factors they suggest has no theoretical justification. The Hull method, however, suggested retaining the number of factors that the theoretical background to the inventory actually proposes.

Social Cognition Impairments in Relation to General Cognitive Deficits, Injury Severity, and Prefrontal Lesions in Traumatic Brain Injury Patients

Impairments in social behavior are frequently found in moderate to severe traumatic brain injury (TBI) patients and are associated with an unfavorable outcome with regard to return to work and social reintegration. Neuropsychological tests measuring aspects of social cognition are thought to be sensitive to these problems. However, little is known about the effect of general cognitive problems on these tests, nor about their sensitivity to injury severity and frontal lesions. In the present study 28 chronic TBI patients with a moderate to severe TBI were assessed with tests for social cognition (emotion recognition, Theory of Mind, and empathy), and for general, non-social cognition (memory, mental speed, attention, and executive function). The patients performed significantly worse than healthy controls on all measures, with the highest effect size for the emotion recognition test, the Facial Expressions of Emotion-Stimuli and Tests (FEEST). Correlation analyses yielded no significant (partial) correlations between social and non-social cognition tests. Consequently, poor performance on social cognition tests was not due to general cognitive deficits. In addition, the emotion recognition test was the only measure that was significantly related to post-traumatic amnesia (PTA) duration, Glasgow Coma Scale (GCS) score, and the presence of prefrontal lesions. Hence, we conclude that social cognition tests are a valuable supplement to a standard neuropsychological examination, and we strongly recommend the incorporation of measurements of social cognition in clinical practice. Preferably, a broader range of social cognition tests would be applied, since our study demonstrated that each of the measures represents a unique aspect of social cognition, but if capacity is limited, at least a test for emotion recognition should be included.

Multilevel component analysis

A general framework for the exploratory component analysis of multilevel data (MLCA) is proposed. In this framework, a separate component model is specified for each group of objects at a certain level. The similarities between the groups of objects at a given level can be expressed by imposing constraints on component models of the groups using the approach adopted in simultaneous component analysis. The constraints used are based on the loading matrices and on the covariances of the component scores of each group. MLCA is related to three-way component analysis and to currently available multilevel structural equation models. It is shown that the latter are less flexible than MLCA. The use of MLCA is illustrated by means of an empirical example.

The predictive value of measures of social cognition for community functioning in schizophrenia: implications for neuropsychological assessment.

The objective of this study was to examine the unique contribution of social cognition to the prediction of community functioning and to explore the relevance of social cognition for clinical practice. Forty-six schizophrenia patients and 53 healthy controls were assessed with tests of social cognition [emotion perception and Theory of Mind (ToM)], general cognition, and, within the patient sample, psychiatric symptoms. Community functioning was rated by nurses or family members. Social cognition was a better predictor of community functioning than general cognition or psychiatric symptoms. When the contributions of emotion perception and ToM were examined separately, only ToM contributed significantly to the prediction of community functioning. Independent living skills were poor in patients with impaired social cognition. In controls, social cognition was not related to community functioning. ToM was the best predictor of community functioning in schizophrenia. However, to fully understand a patients strengths and weaknesses, assessment of social cognition should always be combined with assessment of general cognition and psychiatric symptoms.

The efficacy of SMS text messages to compensate for the effects of cognitive impairments in schizophrenia

BACKGROUND AND AIMS Many people with schizophrenia have severe cognitive impairments that hamper their activities. The effect of pharmacological and behavioural interventions on cognitive functioning has been demonstrated, but even after successful intervention considerable impairments can remain. Therefore, we sought for alternative ways to help patients cope with the effects of their cognitive impairments. In the present study, we have evaluated the efficacy of short message service (SMS) text messages to compensate for the effects of cognitive impairments in schizophrenia in daily life. DESIGN A waiting list controlled trial was conducted: patients were quasi-randomly assigned to an A-B-A (baseline-intervention-follow-up) condition or an A-A-B-A condition that included an additional 7-week waiting list. The waiting list was included to control for the effect of time on relevant outcome. METHOD Sixty-two people with schizophrenia or related psychotic disorders were included in the study. All patients showed impaired goal-directed behaviour in daily life-situations. Patients were prompted with SMS text messages to improve their everyday functioning. The primary outcome measure was the percentage of goals achieved. RESULTS The overall percentage of goals achieved increased with prompting, while performance dropped to baseline level after withdrawing the prompts. Keeping appointments with mental health workers and carrying out leisure activities increased with prompting, while medication adherence and attendance at training sessions remained unchanged. A majority of the patients enjoyed receiving the SMS text messages. DISCUSSION Prompting can significantly improve achievement of a number of relevant goals. For other goals, combining prompting with interventions that enhance motivation seems indicated.

Universal Intracultural and Intercultural Dimensions of the Recalled Frequency of Emotional Experience

This study examined the relative contribution and the nature of dimensions underlying intracultural and intercultural differences in the recalled frequency of emotional experience. From 48 nations, 9,300 participants provided self-reports of the frequency of experienced emotions and several other variables relevant to emotional experience. The data were analyzed by means of multilevel component analysis, which decomposes the data into intracultural and intercultural components. The results showed that positive affect and negative affect emerged as universal dimensions underlying intracultural differences, accounting for the relatively largest part of variance in the data (40%). These dimensions were related to life satisfaction and other variables reflecting positive and negative affectivity. Two dimensions, reflecting positive emotions and inter-personal (negative) emotions, emerged as dimensions underlying nation-level differences, accounting for a smaller proportion of the variance (6%). Intercultural differences on these dimensions were related to nation-level life satisfaction, individualism, and the cultural appropriateness of experiencing corresponding emotions. Differences among individuals affect recalled emotional experience to a greater extent than differences among nations.

Smoothing waves in array CGH tumor profiles

MOTIVATION Many high-resolution array comparative genomic hybridization tumor profiles contain a wave bias, which makes accurate detection of breakpoints in such profiles more difficult. RESULTS An efficient and highly effective algorithm that largely removes the wave bias from tumor profiles by regressing the tumor profile data on data of profiles from the clinical genetics practice. Results are illustrated on two independent datasets. The algorithm is shown to be robust against the presence of true copy number aberrations. Moreover, the smoothed profiles are able to recapitulate the aberration location and signal for simulated tumor profiles. AVAILABILITY Easy-to-use R scripts, user instructions and data are available from http://www.few.vu.nl/~mavdwiel/nowaves.html. SUPPLEMENTARY INFORMATION Supplementary information are available at Bioinformatics online.