scholarly journals The Epworth Sleepiness Scale: Validation of One-Dimensional Factor Structure in a Large Clinical Sample

2018 ◽  
Vol 14 (08) ◽  
pp. 1293-1301 ◽  
Author(s):  
Brittany R. Lapin ◽  
James F. Bena ◽  
Harneet K. Walia ◽  
Douglas E. Moul
Keyword(s):  
Factor Structure ◽  
Epworth Sleepiness Scale ◽  
Scale Validation ◽  
Clinical Sample ◽  
One Dimensional

scholarly journals 0665 FACTOR STRUCTURE OF THE EPWORTH SLEEPINESS SCALE (ESS) IN A LARGE CLINICAL SAMPLE

SLEEP ◽  
2017 ◽  
Vol 40 (suppl_1) ◽  
pp. A246-A246
Author(s):  
DE Moul ◽  
J Urchek ◽  
JF Bena
Keyword(s):  
Factor Structure ◽  
Epworth Sleepiness Scale ◽  
Clinical Sample

Exploring the SPECTRA: Indices of Psychopathology's (SPECTRA) hierarchical factor structure in a clinical sample

2021 ◽  
Vol 179 ◽  
pp. 110946
Author(s):  
Mark A. Blais ◽  
Michelle B Stein ◽  
Samuel Justin Sinclair ◽  
Jared Ruchensky
Keyword(s):  
Factor Structure ◽  
Clinical Sample

Factor structure and psychometric properties of the new multidimensional depression scale in a non-clinical sample

2016 ◽  
Vol 22 (1) ◽  
pp. 63-71
Author(s):  
Mohammad Darharaj ◽  
Mojtaba Habibi ◽  
Michael J. Power ◽  
Sanaz Pirirani ◽  
Faezeh Tehrani
Keyword(s):  
Psychometric Properties ◽  
Factor Structure ◽  
Clinical Sample ◽  
Depression Scale

scholarly journals Empirical Approach to Developing an Optimal Socioeconomic Status Index for Health Surveillance

2022 ◽  
Author(s):  
Jordana LaFantasie ◽  
Francis Boscoe
Keyword(s):  
Socioeconomic Status ◽  
Factor Analysis ◽  
Factor Structure ◽  
Factor Model ◽  
Health Surveillance ◽  
Cancer Surveillance ◽  
Income Poverty ◽  
One Dimensional ◽  
Indicator Variables ◽  
Statistical Consistency

The association between multi-dimensional deprivation and public health is well established, and many area-based indices have been developed to measure or account for socioeconomic status in health surveillance. The Yost Index, developed in 2001, has been adopted in the US for cancer surveillance and is based on the combination of two heavily weighted (household income, poverty) and five lightly weighted (rent, home value, employment, education and working class) indicator variables. Our objectives were to 1) update indicators and find a more parsimonious version of the Yost Index by examining potential models that included indicators with more balanced weights/influence and reduced redundancy and 2) test the statistical consistency of the factor upon which the Yost Index is based. Despite the usefulness of the Yost Index, a one-factor structure including all seven Yost indicator variables is not statistically reliable and should be replaced with a three-factor model to include the true variability of all seven indicator variables. To find a one-dimensional alternative, we conducted maximum likelihood exploratory factor analysis on a subset of all possible combinations of fourteen indicator variables to find well-fitted one-dimensional factor models and completed confirmatory factor analysis on the resulting models. One indicator combination (poverty, education, employment, public assistance) emerged as the most stable unidimensional model. This model is more robust to extremes in local cost of living conditions, is comprised of ACS variables that rarely require imputation by the end-user and is a more parsimonious solution than the Yost index with a true one-factor structure.

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.

Exploring the factor structure of the mini-ICF-APP in an inpatient clinical sample, according to the psychiatric diagnosis

2021 ◽  
Vol 14 (4) ◽  
pp. 186-195
Author(s):  
Stephan T. Egger ◽  
Godehard Weniger ◽  
Julio Bobes ◽  
Erich Seifritz ◽  
Stefan Vetter
Keyword(s):  
Factor Structure ◽  
Psychiatric Diagnosis ◽  
Clinical Sample

The Posttraumatic Growth Inventory: Factor Structure and Invariance in a Sample of Breast Cancer Patients and in a Non-Clinical Sample

2016 ◽  
Vol 19 ◽  
Author(s):  
Catarina Ramos ◽  
Isabel Leal ◽  
Ana Lúcia Marôco ◽  
Richard G. Tedeschi
Keyword(s):  
Breast Cancer ◽  
Posttraumatic Growth ◽  
Cancer Patients ◽  
Factor Structure ◽  
Goodness Of Fit ◽  
Clinical Sample ◽  
Five Factor Model ◽  
Breast Cancer Patients ◽  
Factor Loadings ◽  
Posttraumatic Growth Inventory

AbstractThe Posttraumatic Growth Inventory (PTGI) is frequently used to assess positive changes following a traumatic event. The aim of the study is to examine the factor structure and the latent mean invariance of PTGI. A sample of 205 (Mage = 54.3, SD = 10.1) women diagnosed with breast cancer and 456 (Mage = 34.9, SD = 12.5) adults who had experienced a range of adverse life events were recruited to complete the PTGI and a socio-demographic questionnaire. We use Confirmatory Factor Analysis (CFA) to test the factor-structure and multi-sample CFA to examine the invariance of the PTGI between the two groups. The goodness of fit for the five-factor model is satisfactory for breast cancer sample (χ2(175) = 396.265; CFI = .884; NIF = .813; RMSEA [90% CI] = .079 [.068, .089]), and good for non-clinical sample (χ2(172) = 574.329; CFI = .931; NIF = .905; RMSEA [90% CI] = .072 [.065, .078]). The results of multi-sample CFA show that the model fit indices of the unconstrained model are equal but the model that uses constrained factor loadings is not invariant across groups. The findings provide support for the original five-factor structure and for the multidimensional nature of posttraumatic growth (PTG). Regarding invariance between both samples, the factor structure of PTGI and other parameters (i.e., factor loadings, variances, and co-variances) are not invariant across the sample of breast cancer patients and the non-clinical sample.

Factor Structure of the Automatic Thoughts Questionnaire in a Clinical Sample

2013 ◽  
Vol 6 (3) ◽  
pp. 280-291 ◽  
Author(s):  
Robert D. Zettle ◽  
Blake K. Webster ◽  
Suzanne R. Gird ◽  
Alexandra L. Wagener ◽  
Charles A. Burdsal
Keyword(s):  
Factor Structure ◽  
Clinical Sample ◽  
Automatic Thoughts
Sign in / Sign up

Export Citation Format

Share Document