The Split Population Logit (SPopLogit): Modeling Measurement Bias in Binary Data

2011 ◽  
Author(s):  
Andreas Beger ◽  
Jacqueline H.R. DeMeritt ◽  
Wonjae Hwang ◽  
Will H. Moore
Keyword(s):  
Binary Data ◽  
Measurement Bias

The Beck Hopelessness Scale

2016 ◽  
Vol 32 (2) ◽  
pp. 111-118 ◽  
Author(s):  
Marianna Szabó ◽  
Veronika Mészáros ◽  
Judit Sallay ◽  
Gyöngyi Ajtay ◽  
Viktor Boross ◽  
...  
Keyword(s):  
Binary Data ◽  
Factor Model ◽  
Clinical Sample ◽  
Analytical Techniques ◽  
Cross Cultural ◽  
Cultural Validity ◽  
Beck Hopelessness Scale ◽  
Method Effects ◽  
Hungarian Population ◽  
Components Analysis

Abstract. The aim of the present study was to examine the construct and cross-cultural validity of the Beck Hopelessness Scale (BHS; Beck, Weissman, Lester, & Trexler, 1974 ). Beck et al. applied exploratory Principal Components Analysis and argued that the scale measured three specific components (affective, motivational, and cognitive). Subsequent studies identified one, two, three, or more factors, highlighting a lack of clarity regarding the scale’s construct validity. In a large clinical sample, we tested the original three-factor model and explored alternative models using both confirmatory and exploratory factor analytical techniques appropriate for analyzing binary data. In doing so, we investigated whether method variance needs to be taken into account in understanding the structure of the BHS. Our findings supported a bifactor model that explicitly included method effects. We concluded that the BHS measures a single underlying construct of hopelessness, and that an incorporation of method effects consolidates previous findings where positively and negatively worded items loaded on separate factors. Our study further contributes to establishing the cross-cultural validity of this instrument by showing that BHS scores differentiate between depressed, anxious, and nonclinical groups in a Hungarian population.

An exploration of measurement bias due to worry and emotionality components of test anxiety on cognitive ability tests

2011 ◽  
Author(s):  
Georgia Papantoniou ◽  
Despina Moraitou ◽  
Dimitra Filippidou ◽  
Magda Dinou ◽  
Effie Katsadima
Keyword(s):  
Cognitive Ability ◽  
Test Anxiety ◽  
Measurement Bias ◽  
Cognitive Ability Tests ◽  
Ability Tests

Psychological networks in clinical populations: investigating the consequences of Berkson's bias

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jill de Ron ◽  
Eiko I. Fried ◽  
Sacha Epskamp
Keyword(s):  
Network Structure ◽  
Binary Data ◽  
Ordinal Data ◽  
Graphical Model ◽  
Rating Scale ◽  
Network Models ◽  
Simulation Studies ◽  
Potential Threat ◽  
Sum Score ◽  
Research Populations

Abstract Background In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson's bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson's bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data. Methods In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants. Results The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson's bias literature, selection reduced recovery rates by inducing negative connections between the items. Conclusion Our findings provide evidence that Berkson's bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson's bias and their pitfalls.

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