scholarly journals Socioeconomic and gender group differences in early literacy skills: a multiple-group confirmatory factor analysis approach

2015 ◽  
Vol 21 (1) ◽  
pp. 40-59 ◽  
Author(s):  
Julia Ai Cheng Lee ◽  
Stephanie Al Otaiba
2017 ◽  
Vol 121 (3) ◽  
pp. 548-565 ◽  
Author(s):  
Rina S. Fox ◽  
Teresa A. Lillis ◽  
James Gerhart ◽  
Michael Hoerger ◽  
Paul Duberstein

The DASS-21 is a public domain instrument that is commonly used to evaluate depression and anxiety in psychiatric and community populations; however, the factor structure of the measure has not previously been examined in oncologic settings. Given that the psychometric properties of measures of distress may be compromised in the context of symptoms related to cancer and its treatment, the present study evaluated the psychometric properties of the DASS-21 Depression and Anxiety scales in cancer patients ( n = 376) as compared to noncancer control participants ( n = 207). Cancer patients ranged in age from 21 to 84 years (mean = 58.3, standard deviation = 10.4) and noncancer control participants ranged in age from 18 to 81 years (mean = 45.0, standard deviation = 11.7). Multiple group confirmatory factor analysis supported the structural invariance of the DASS-21 Depression and Anxiety scales across groups; the factor variance/covariance invariance model was the best fit to the data. Cronbach’s coefficient alpha values demonstrated acceptable internal consistency reliability across the total sample as well as within subgroups of cancer patients and noncancer control participants. Expected relationships of DASS-21 Depression and Anxiety scale scores to measures of suicidal ideation, quality of life, self-rated health, and depressed mood supported construct validity. These results support the psychometric properties of the DASS-21 Depression and Anxiety scales when measuring psychological distress in cancer patients.


PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0181908 ◽  
Author(s):  
Mia Scheffers ◽  
Marijtje A. J. van Duijn ◽  
Ruud J. Bosscher ◽  
Durk Wiersma ◽  
Robert A. Schoevers ◽  
...  

Affilia ◽  
2018 ◽  
Vol 34 (1) ◽  
pp. 83-98 ◽  
Author(s):  
Sunday B. Fakunmoju ◽  
Tina Abrefa-Gyan ◽  
Ntandoyenkosi Maphosa

Research scales developed in one society are often validated in another society to determine the factor structure and measurement equivalence of the scales. Using a convenience sample of 378 respondents from two cross-sectional studies, the present analyses examined confirmatory factor analysis (CFA) and gender invariance in the Illinois Rape Myth Acceptance (IRMA) Scale in Nigeria. Specifically, the analyses examined whether the scale holds similar factor structure, whether the latent means can be compared, and whether respondents interpreted items similarly or ascribed the same meaning to them across gender. Based on the analyses, CFA results validated the hypothesized multidimensional four-factor structure of IRMA, namely, “she asked for it,” “he didn’t mean to,” “it wasn’t really rape,” and “she lied.” Similarly, the IRMA measurement was invariant (partial scalar invariance) across gender, suggesting that men and women interpreted IRMA’s items and constructs similarly. Results of an independent-samples t test suggested that women were more likely than men to reject the myth that female victim of rape “lied.” In general, preliminary findings indicated that IRMA is suitable for research on rape myths in Nigeria. Knowledge generated from its use may enhance understanding of rape myths, rape-supportive behaviors, and rape prevention and victim intervention programs.


2008 ◽  
Vol 68 (6) ◽  
pp. 923-939 ◽  
Author(s):  
Ilse Stuive ◽  
Henk A. L. Kiers ◽  
Marieke E. Timmerman ◽  
Jos M. F. ten Berge

This study compares two confirmatory factor analysis methods on their ability to verify whether correct assignments of items to subtests are supported by the data. The confirmatory common factor (CCF) method is used most often and defines nonzero loadings so that they correspond to the assignment of items to subtests. Another method is the oblique multiple group (OMG) method, which defines subtests as unweighted sums of the scores on all items assigned to the subtest, and (corrected) correlations are used to verify the assignment. A simulation study compares both methods, accounting for the influence of model error and the amount of unique variance. The CCF and OMG methods show similar behavior with relatively small amounts of unique variance and low interfactor correlations. However, at high amounts of unique variance and high interfactor correlations, the CCF detected correct assignments more often, whereas the OMG was better at detecting incorrect assignments.


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