scholarly journals Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random

2021 ◽  
Author(s):  
Myrsini Katsikatsou ◽  
Irini Moustaki ◽  
Haziq Jamil
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Likelihood Estimation ◽  
Missing At Random ◽  
Categorical Variables ◽  
Pairwise Likelihood ◽  
Confirmatory Factor ◽  
Analysis Models

Giving G a Meaning: An Application of the Bifactor-(S-1) Approach to Realize a More Symptom-Oriented Modeling of the Beck Depression Inventory–II

Assessment ◽  
2018 ◽  
Vol 27 (7) ◽  
pp. 1429-1447 ◽  
Author(s):  
Manuel Heinrich ◽  
Pavle Zagorscak ◽  
Michael Eid ◽  
Christine Knaevelsrud
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Beck Depression Inventory ◽  
Factor Structure ◽  
Clinical Sample ◽  
First Order ◽  
Confirmatory Factor ◽  
Bifactor Models ◽  
Current Article ◽  
Analysis Models

The Beck Depression Inventory–II is one of the most frequently used scales to assess depressive burden. Despite many psychometric evaluations, its factor structure is still a topic of debate. An increasing number of articles using fully symmetrical bifactor models have been published recently. However, they all produce anomalous results, which lead to psychometric and interpretational difficulties. To avoid anomalous results, the bifactor-(S-1) approach has recently been proposed as alternative for fitting bifactor structures. The current article compares the applicability of fully symmetrical bifactor models and symptom-oriented bifactor-(S-1) and first-order confirmatory factor analysis models in a large clinical sample ( N = 3,279) of adults. The results suggest that bifactor-(S-1) models are preferable when bifactor structures are of interest, since they reduce problematic results observed in fully symmetrical bifactor models and give the G factor an unambiguous meaning. Otherwise, symptom-oriented first-order confirmatory factor analysis models present a reasonable alternative.

scholarly journals Pairwise likelihood estimation for factor analysis models with ordinal data

2012 ◽  
Vol 56 (12) ◽  
pp. 4243-4258 ◽  
Author(s):  
Myrsini Katsikatsou ◽  
Irini Moustaki ◽  
Fan Yang-Wallentin ◽  
Karl G. Jöreskog
Keyword(s):  
Factor Analysis ◽  
Ordinal Data ◽  
Likelihood Estimation ◽  
Pairwise Likelihood ◽  
Analysis Models

scholarly journals Establishing the HLS-M-Q18 short version of the European Health Literacy survey questionnaire for the Malaysian context

2020 ◽  
Author(s):  
Emma Mohamad ◽  
Manimaran Krishnan Kaundan ◽  
Mohammad Rezal Hamzah ◽  
Arina Anis Azlan ◽  
Suffian Hadi Ayub ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Health Literacy ◽  
Confirmatory Factor Analysis ◽  
Categorical Variables ◽  
Short Version ◽  
Survey Questionnaire ◽  
Confirmatory Factor ◽  
Literacy Survey ◽  
European Health ◽  
Item Scale

Abstract Background: The European Health Literacy Survey Questionnaire (HLS-EU-Q47) is becoming a widely used tool to measure health literacy (HL), including in Malaysia. There are efforts to reduce the 47-item scale to parsimonious short item scales that still reflect the assumptions and requirements of the conceptual model. This study used confirmatory factor analysis to reduce the 47-item scale to a short scale that can offer a feasible HL screening tool with sufficient psychometric properties. Methods: A cross-sectional survey was conducted on the Malaysian population based on ethnic distribution to ensure that the short version instrument reflects the country’s varied ethnicities. The survey was administered by well-trained interviewers working for the Ministry of Health Malaysia. A total of 866 responses were obtained. Data was analysed using multi-factorial confirmatory factor analysis (CFA) with categorical variables. Results: The analysis resulted in a satisfactory 18-item model. There were high correlations among the 18 items. The internal consistency reliability was robust, with no floor/ceiling effects. These results represented equivalence and consistency among the responses to items, suggesting that these items were homogenous in measuring Malaysian health literacy. The strong convergent and discriminant validity of the model makes the proposed 18 items a suitable short version of the health literacy instrument for Malaysia. Conclusions: The researchers propose the 18-item instrument to be named HLS-M-Q18. This short version instrument may be used in measuring health literacy in Malaysia as it achieved robust reliability, structural validity and construct validity that fulfilled goodness-of-fit criteria.

scholarly journals Modeling the construct validity of the Berlin Intelligence Structure Model

2015 ◽  
Vol 32 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Heinz-Martin Süß ◽  
André Beauducel
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Construct Validity ◽  
Model Test ◽  
Representative Sample ◽  
Integrative Model ◽  
Structure Model ◽  
Confirmatory Factor ◽  
Analysis Models ◽  
High Degree

The Berlin Intelligence Structure Model is a hierarchical and faceted model which is originally based on an almost representative sample of tasks found in the literature. Therefore, the Berlin Intelligence Structure Model is an integrative model with a high degree of generality. The present paper investigates the construct validity of this model by using different confirmatory factor analysis models. The results show that the model assumptions are supported only in part by the data. Moreover, it is demonstrated that there are different possibilities to incorporate the Berlin Intelligence Structure Model assumptions into confirmatory factor analysis models. The results are discussed with regard to the validity of the Berlin Intelligence Structure Model test, and the validity of the model.

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