Changing explicit and implicit attitudes: the case of self-esteem

Three experiments investigated predictions concerning asymmetrical patterns of implicit and explicit self-esteem change. Specifically, we investigated the influence of knowledge about the own self that is momentarily salient as well as the influence of affective valence associated with the self in memory on implicit and explicit self-esteem. The latter was induced by evaluative conditioning, the former by directed thinking about oneself. We found that while evaluative conditioning changed implicit but not explicit self-esteem (Experiment 1), thinking about the own self altered explicit but not implicit self-esteem (Experiment 2). Moreover, in a third experiment, it could be shown that the effect of evaluative conditioning can spill over to the explicit level when participants are asked to focus on their feelings prior to making their self-report judgements (Experiment 3). Implications of our results are discussed in terms of recent controversies regarding dual process models of attitudes and associative versus propositional modes of information processing

Changes in size and interpretation of parameter estimates in within-person models in the presence of time-invariant and time-varying covariates

For several decades, cross-lagged panel models (CLPM) have been the dominant statistical model in relationship research for investigating reciprocal associations between two (or more) constructs over time. However, recent methodological research has questioned the frequent usage of the CLPM because, amongst other things, the model commingles within-person associations with between-person associations, while most developmental research questions pertain to within-person processes. Furthermore, the model presumes that there are no third variables that confound the relationships between the longitudinally assessed variables. Therefore, the usage of alternative models such as the Random-Intercept Cross-Lagged Panel Model (RI-CLPM) or the Latent Curve Model with Structured Residuals (LCM-SR) has been suggested. These models separate between-person from within-person variation and they also control for time constant covariates. However, there might also be third variables that are not stable but rather change across time and that can confound the relationships between the variables studied in these models. In the present article, we explain the differences between the two types of confounders and investigate how they affect the parameter estimates of within-person models such as the RI-CLPM and the LCM-SR

Testing Similarity Effects with Dyadic Response Surface Analysis

Dyadic similarity effect hypotheses state that the (dis)similarity between dyad members (e.g. the similarity on a personality dimension) is related to a dyadic outcome variable (e.g. the relationship satisfaction of both partners). Typically, these hypotheses have been investigated by using difference scores or other profile similarity indices as predictors of the outcome variables. These approaches, however, have been vigorously criticized for their conceptual and statistical shortcomings. Here, we introduce a statistical method that is based on polynomial regression and addresses most of these shortcomings: dyadic response surface analysis. This model is tailored for similarity effect hypotheses and fully accounts for the dyadic nature of relationship data. Furthermore, we provide a tutorial with an illustrative example and reproducible R and Mplus scripts that should assist substantive researchers in precisely formulating, testing, and interpreting their dyadic similarity effect hypotheses

Ranking of Dry Ports in Europe - Benchmarking

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International Characteristics of the Macro-Logistics System of Freight Villages

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What IAPT CBT high-intensity trainees do after training

Background: The UK Department of Health Improving Access to Psychological Therapies (IAPT) initiative set out to train a large number of therapists in cognitive behaviour therapies (CBT) for depression and anxiety disorders. Little is currently known about the retention of IAPT CBT trainees, or the use of CBT skills acquired on the course in the workplace after training has finished. Aims: This study set out to conduct a follow-up survey of past CBT trainees on the IAPT High Intensity CBT Course at the Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), one of the largest IAPT High Intensity courses in the UK. Method: Past trainees (n = 212) across 6 cohorts (2008-2014 intakes) were contacted and invited to participate in a follow-up survey. A response rate of 92.5% (n = 196) was achieved. Results: The vast majority of IAPT trainees continue to work in IAPT services posttraining (79%) and to practise CBT as their main therapy modality (94%); 61% have become CBT supervisors. A minority (23%) have progressed to other senior roles in the services. Shortcomings are reported in the use of out-of-office CBT interventions, the use of disorder-specific outcome measures and therapy recordings to inform therapy and supervision. Conclusions: Past trainees stay working in IAPT services and continue to use CBT methods taught on the course. Some NICE recommended treatment procedures that are likely to facilitate patients’ recovery are not being routinely implemented across IAPT services. The results have implications for the continued roll out of the IAPT programme, and other future large scale training initiatives

Development of dry ports in Europe

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