Educationally corrective experiences as a common factor in psychotherapy supervision.

Abstract. Background: The ability to predict imminent risk of suicide is limited, particularly among mental health clients. Root cause analysis (RCA) can be used by health services to identify service-wide approaches to suicide prevention. Aims: To (a) develop a standardized taxonomy for RCAs; (b) to quantitate service-related factors associated with suicides; and (c) to identify service-related suicide prevention strategies. Method: The RCAs of all people who died by suicide within 1 week of contact with the mental health service over 5 years were thematically analyzed using a data collection tool. Results: Data were derived from RCAs of all 64 people who died by suicide between 2008 and 2012. Major themes were categorized as individual, situational, and care-related factors. The most common factor was that clients had recently denied suicidality. Reliance on carers, recent changes in medication, communication problems, and problems in follow-through were also commonly identified. Conclusion: Given the difficulty in predicting suicide in people whose expressions of suicidal ideation change so rapidly, services may consider the use of strategies aimed at improving the individual, stressor, support, and care factors identified in this study.

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From Bi-Dimensionality to Uni-Dimensionality in Self-Report Questionnaires

European Journal of Psychological Assessment ◽

10.1027/1015-5759/a000583 ◽

2020 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Bjarne Schmalbach ◽

Markus Zenger ◽

Michalis P. Michaelides ◽

Karin Schermelleh-Engel ◽

Andreas Hinz ◽

...

Keyword(s):

Factor Model ◽

Common Factor ◽

Self Regulation ◽

Model Fit ◽

Self Report ◽

External Criterion ◽

Common Factor Model ◽

Random Intercept ◽

Using Data ◽

Core Self

Abstract. The common factor model – by far the most widely used model for factor analysis – assumes equal item intercepts across respondents. Due to idiosyncratic ways of understanding and answering items of a questionnaire, this assumption is often violated, leading to an underestimation of model fit. Maydeu-Olivares and Coffman (2006) suggested the introduction of a random intercept into the model to address this concern. The present study applies this method to six established instruments (measuring depression, procrastination, optimism, self-esteem, core self-evaluations, and self-regulation) with ambiguous factor structures, using data from representative general population samples. In testing and comparing three alternative factor models (one-factor model, two-factor model, and one-factor model with a random intercept) and analyzing differential correlational patterns with an external criterion, we empirically demonstrate the random intercept model’s merit, and clarify the factor structure for the above-mentioned questionnaires. In sum, we recommend the random intercept model for cases in which acquiescence is suspected to affect response behavior.

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Comparison of Principal Component Solutions in Two Populations

Methodology ◽

10.1027/1614-2241/a000099 ◽

2016 ◽

Vol 12 (1) ◽

pp. 11-20 ◽

Cited By ~ 1

Author(s):

Gregor Sočan

Keyword(s):

Simulation Study ◽

Principal Components ◽

Common Factor ◽

Principal Component ◽

Component Model ◽

Bootstrap Procedure ◽

Factor Loadings ◽

Component Loadings ◽

Principal Component Model ◽

Two Populations

Abstract. When principal component solutions are compared across two groups, a question arises whether the extracted components have the same interpretation in both populations. The problem can be approached by testing null hypotheses stating that the congruence coefficients between pairs of vectors of component loadings are equal to 1. Chan, Leung, Chan, Ho, and Yung (1999) proposed a bootstrap procedure for testing the hypothesis of perfect congruence between vectors of common factor loadings. We demonstrate that the procedure by Chan et al. is both theoretically and empirically inadequate for the application on principal components. We propose a modification of their procedure, which constructs the resampling space according to the characteristics of the principal component model. The results of a simulation study show satisfactory empirical properties of the modified procedure.

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