scholarly journals The negative consequences of hypersexuality: Revisiting the factor structure of the Hypersexual Behavior Consequences Scale and its correlates in a large, non-clinical sample

2021 ◽  
Vol 13 ◽  
pp. 100321
Author(s):  
Mónika Koós ◽  
Beáta Bőthe ◽  
Gábor Orosz ◽  
Marc N. Potenza ◽  
Rory C. Reid ◽  
...  
Keyword(s):  
Factor Structure ◽  
Clinical Sample ◽  
Negative Consequences ◽  
Hypersexual Behavior

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

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

scholarly journals Development and Psychometric Properties of NEGORI (Negative Orientation Questionnaire)

2018 ◽  
Vol 9 ◽  
Author(s):  
László Kasik ◽  
Zita Gál ◽  
Edit Tóth
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Structural Equation ◽  
Age Groups ◽  
Questionnaire Development ◽  
Equation Model ◽  
Social Problem Solving ◽  
Negative Consequences ◽  
Good Reliability ◽  
Negative Orientation

There are several available questionnaires that measure negative problem orientation with one factor. Our aim was to create a multifactor questionnaire that enables a more detailed and reliable analysis of interpersonal problems and one's negative orientation toward their solutions in adolescence. We carried out two data collections during the development of the questionnaire (N2016 = 952, N2017 = 835) among 12-, 15- and 18-year-olds. The tested statements were chosen based on the category system of an earlier pilot research, as well as students’ and professionals’ statements, which were then organised into a factor structure. To analyse convergent and discriminative validity, the Social Problem-Solving Inventory — Revised (SPSI-R; D'Zurilla et al., 2002) was used. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) greatly support the theoretical factor structure, and the structural equation model (SEM) also confirmed what had been thought about the system of connections of the latent dimensions. The result of the questionnaire development is the 21-item, six-factor NEGORI (Negative Orientation Questionnaire), which bears good reliability indexes in all age groups and can measure the following aspects within negative orientation: negative self-efficacy; negative consequences; positive consequences; habits, pattern; waiting; fending off the problem. We gained different and more detailed information as compared to previous data related to age and gender.

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

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
Sign in / Sign up

Export Citation Format

Share Document