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Scientometric results on publication trends in clinical psychology, which refer to publication type and methodology of case studies/reports, are presented. Absolute and relative frequencies of clinical case studies are identified for the segment “mental and behavioral disorders” in MEDLINE (ICD-10 Chapter V [F]) as well as for clinical psychology publications documented in PsycINFO and PSYNDEX in 40 publication years (1975-2014). Results show an increase of the absolute number of published case studies documented in MEDLINE and PsycINFO (but not in PSYNDEX), which is highly correlated with the total increase of clinical psychology publications in both databases. Relative frequencies show another picture, namely a drop of the percentage of case studies on mental and behavioral disorders in MEDLINE, and a sharp drop in PSYNDEX since the 1980s. The trend for the relative frequency of case studies within all publications on clinical psychology documented in PsycINFO is V-shaped with 6% in the 1970s, 3% in the early 1990s, and 4-5% after the millennium. Pros and cons of case studies in clinical psychology research and education are discussed. Qualitative and quantitative case study methodologies are distinguished with respect to the phases of clinical trials and observational studies in evidence-based and empirically supported psychotherapy. Subsequently, methodological constraints are balanced with specific values in clinical training, applied research, and innovative research on the symptomatology, etiology, and classification of mental disorders as well as on combined and/or integrative treatment techniques and methods.
The paper reports an investigation on whether valid results can be achieved in analyzing the structure of datasets although a large percentage of data is missing without replacement. Two types of confirmatory factor analysis (CFA) models were employed for this purpose: the missing data CFA model with an additional latent variable for representing the missing data and the semi-hierarchical CFA model that also includes the additional latent variable and reflects the hierarchical structure assumed to underlie the data. Whereas, the missing data CFA model assumes that the model is equally valid for all participants, the semi-hierarchical CFA model is implicitly specified differently for subgroups of participants with and without omissions. The comparison of these models with the regular one-factor model in investigating simulated binary data revealed that the modeling of missing data prevented negative effects of missing data on model fit. The investigation of the accuracy in estimating the factor loadings yielded the best results for the semi-hierarchical CFA model. The average estimated factor loadings for items with and without omissions showed the expected equal sizes. But even this model tended to underestimate the expected values.
We investigated whether dichotomous data showed the same latent structure as the interval-level data from which they originated. Given constancy of dimensionality and factor loadings reflecting the latent structure of data, the focus was on the variance of the latent variable of a confirmatory factor model. This variance was shown to summarize the information provided by the factor loadings. The results of a simulation study did not reveal exact correspondence of the variances of the latent variables derived from interval-level and dichotomous data but shrinkage. Since shrinkage occurred systematically, methods for recovering the original variance were fleshed out and evaluated.