A Bayesian approach with propensity scores for prediction of unknown responses

2021 ◽  
Vol 32 (6) ◽  
pp. 1353-1362
Author(s):  
Juhee Lee ◽  
Eun Jin Jang ◽  
Dal Ho Kim
Keyword(s):  
Bayesian Approach ◽  
Propensity Scores

Klinische und psychosoziale Charakteristika bei Suchtpatienten mit und ohne PTBS

2017 ◽  
Vol 63 (6) ◽  
pp. 315-324
Author(s):  
Anja Hildebrand ◽  
Heinz C. Vollmer ◽  
Julia Domma-Reichart
Keyword(s):  
Propensity Scores

Zusammenfassung. Hintergrund: In Deutschland liegen nur vereinzelte Studien zur PTBS-Prävalenzquote bei Suchtpatienten und zu deren psychischen Befund vor. Fragestellung: Wie hoch ist die relative Häufigkeit einer PTBS bei Patienten mit substanzbezogenen Störungen und wie unterscheiden sich die Patienten mit und ohne PTBS hinsichtlich klinischer und psychosozialer Charakteristika? Methode: Mittels Chi-Quadrat- und t-Tests wurden 376 mittels Propensity Scores gematchte Patienten aus einer Stichprobe von 4105 konsekutiv aufgenommenen Abhängigen in diagnostischen und psychischen Merkmalen retrospektiv miteinander verglichen. Ergebnisse: Die relative Häufigkeit von PTBS lag bei den Patienten mit einer alkoholbezogenen Störung bei 3,8 %, bei den restlichen Suchtpatienten mit Störungen durch andere psychotrope Substanzen bei 10,5 %. Bei den PTBS Patienten lag häufiger eine Persönlichkeitsstörung vor. Außerdem waren die PTBS Patienten stärker psychisch belastet, in ihrem Interaktionsstil abweisender, introvertierter, und nachgiebiger sowie im Bindungsstil vermeidender. Schlussfolgerungen: Die Unterschiede verdeutlichen die Notwendigkeit von auf den Interaktions- und Bindungsstil individuell angepassten Interventionen im Rahmen der Standardbehandlungen für Suchtpatienten mit PTBS.

Validating Four Standard Scales in Spiritually Practicing and Nonpracticing Samples Using Propensity Score Matching

2008 ◽  
Vol 24 (3) ◽  
pp. 165-173 ◽  
Author(s):  
Niko Kohls ◽  
Harald Walach
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Repeated Measures ◽  
Propensity Scores ◽  
Total Sample ◽  
Experimental Manipulation ◽  
Brief Symptom Inventory ◽  
Time Interaction ◽  
Social Support Scale ◽  
Lower Test

Validation studies of standard scales in the particular sample that one is studying are essential for accurate conclusions. We investigated the differences in answering patterns of the Brief-Symptom-Inventory (BSI), Transpersonal Trust Scale (TPV), Sense of Coherence Questionnaire (SOC), and a Social Support Scale (F-SoZu) for a matched sample of spiritually practicing (SP) and nonpracticing (NSP) individuals at two measurement points (t1, t2). Applying a sample matching procedure based on propensity scores, we selected two sociodemographically balanced subsamples of N = 120 out of a total sample of N = 431. Employing repeated measures ANOVAs, we found an intersample difference in means only for TPV and an intrasample difference for F-SoZu. Additionally, a group × time interaction effect was found for TPV. While Cronbach’s α was acceptable and comparable for both samples, a significantly lower test-rest-reliability for the BSI was found in the SP sample (rSP = .62; rNSP = .78). Thus, when researching the effects of spiritual practice, one should not only look at differences in means but also consider time stability. We recommend propensity score matching as an alternative for randomization in variables that defy experimental manipulation such as spirituality.

Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat
Keyword(s):  
Task Performance ◽  
Prior Knowledge ◽  
Statistical Learning ◽  
Cognitive Processes ◽  
Bayesian Approach ◽  
Word Identification ◽  
Artificial Language ◽  
Artificial Languages ◽  
Transitional Probabilities ◽  
Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

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