Sentences with phrase «factor analyses»

of persons with vision loss, using the draft instrument and through factor analyses of their responses.

Exploratory and confirmatory factor analyses were conducted; Cronbach's Alpha coefficients were calculated for each subscale; and... intra-class correlation coefficients were calculated for each item.

Six HWQ sub-scales were identified... from factor analyses: productivity, concentration / focus, supervisor relations, impatience / irritability, work satisfaction, and non-work satisfaction.

Parallel analysis was conducted with successive iterations of exploratory and confirmatory factor analyses.

Factor analyses reveal four dimensions that represent cognitive, emotional, physiological, and behavioural levels, respectively.

Given that the MDI's Physical health and well - being domain and the Constructive use of time after school domain consists of individual items, not scales, no factor analyses were conducted.

The exploratory factor analyses in Study 3 indicated that almost all items had the highest factor loadings on the factors corresponding to their respective MDI scales.

For scales that consisted of more than three items, results of previously conducted factor analyses and reliability assessments (i.e., factor loadings, Cronbach's alpha after an item is deleted) were used to guide data reduction, in order to shorten scales to a maximum of three to five items.

To address the second research question in Study 3, an exploratory factor analyses with a maximum likelihood method with Varimax rotation indicated 5 factors that accounted for 47 % of the sample variance.

First, confirmatory factor analyses (CFAs) were conducted in MPlus, to test the model fit of the factor solutions indicated in Study 3, using polychoric correlation matrices, the WLSMV estimation method, and the oblique (geomin) factor rotation.

Exploratory factor analyses conducted with Study 1 and Study 3 data demonstrated general consistency in the underlying factor structure of the SRLTAS and generally supported a five factor solution.

However, after revisions of the response format and the order of items on the MDI, the confirmatory factor analyses in Study 4 indicated excellent model fit for all scales, including the self - concept scale.

Factor analyses from Study 1 supported the multidimensional nature of the instrument.

In conclusion, Exploratory Factor Analyses from data in Study 1 indicated support for five factors: social consequences; to include concerns regarding how parents, friends, classmates and teachers may view test performance; item types; to include items related to anxiety across item formats; and temporal aspects of anxiety; that is how stress is felt before, during, and after an exam.

The authors would like to thank Robert Young, who conducted the confirmatory factor analyses and Sally Macintyre for her comments on an earlier version of this paper.

Six potential factor structures for the 18 items of the SWAN (Strengths and Weaknesses of ADHD - symptoms and Normal - behavior) scale were tested using confirmatory and exploratory factor analyses.

The confirmatory factor analyses have clearly identified each one the eleven factors.

Factor analyses showed that single items and the longer scales loaded on the same factor.

Separate factor analyses were performed for the job characteristics, personality variables, coping scores and the outcomes.

Studies need to employ multi-group confirmatory factor analyses and differential item functioning to test whether measurement models are equivalent across subgroups (e.g., gender) and whether individual items or scales are biased against certain subgroups.

In Study 1, but not Study 3 exploratory factor analyses indicated item 14, can't do part of the test, did not load on any factors.

Factor analyses were then carried out for the multiple - item scores and single item scores from the same group of variables (work demands; resources; personality; coping and outcomes).

Factor analyses reveal four subscales: Subjective, Neurophysiological, Panic, and Autonomic.

The results of confirmatory factor analyses without item parceling were summarized in Table 2.

Moreover, confirmatory factor analyses using parcels revealed that the two - factor model met all cutoff criteria (CFI >.95, TLI >.95, RMSEA <.06) proposed by Hu & Bentler (1999).

Confirmatory factor analyses showed that the two - factor model of the PNS - J fit the data better than the one - factor model, as shown in the studies that validated the original PNS Scale.

The results of confirmatory factor analyses using item parceling techniques were also summarized in Table 2.

Additionally, confirmatory factor analyses using parceling techniques, which aggregate several items into parcels, were performed.

Afterward, confirmatory factor analyses using the 11 items of the PNS - J as indicators were performed to examine whether the two - factor model — 4 items loaded on the Desire for Structure factor and the other 7 items loaded on the Response to Lack of Structure factor — fits the data better than the one - factor model.

Results highlighted a) through exploratory and confirmatory factor analyses, a meaningful six - factor model (emotion expression, task utility self - persuasion, help - seeking, negative self - talk, brief attentional relaxation, and dysfunctional avoidance); b) satisfactory internal reliabilities; c) test - retest reliability scores indicative of a satisfactory stability of the measures over time; d) preliminary evidence of convergent and discriminant validity with CERS - M being very weakly linked to verbal skill and moderately to emotion regulation strategies measured through the Flemish version of the COPE - questionnaire; e) preliminary evidence of criterion validity, with CERS - M scores predicting math anxiety, and to a lesser extent, students» performance; f) preliminary evidence of incremental validity, with the CERS - M predicting math anxiety and performance over and above emotion regulation measured by the COPE - questionnaire.

In general, model fit indexes in confirmatory factor analyses become worse as indicators of latent variables increase (Bandalos, 2002; Coffman & MacCallum, 2005; Gribbons & Hocevar, 1998; Little, Cunningham, Shahar, & Widaman, 2002; Marsh, Hau, Balla, & Grayson, 1998).

Factor analyses support the three - factor structure and studies with other style and psychological variables demonstrate the construct validity of VIEW (Burger, Marino, Ponterotto, & Houtz, 2008; Houtz, 2002; Houtz, Matos, Park, Scheinholtz, & Selby, 2007; Houtz & Selby, 2009; Houtz, Selby, Esquivel, Okoye, Peters, & Treffinger, 2003a, 2003b; Shaw, Selby, & Houtz, 2009).

The results of confirmatory factor analyses revealed that the two - factor model of the PNS - J fit better than the one - factor model, regardless of whether using items or parcels as indicators.

Operationalization was tested using confirmatory factor analyses and causal hypotheses were evaluated by means of structural equation modeling.

Then, to test whether the a priori factor structure would fit the data of mothers and fathers, a two - group confirmatory factor analyses was conducted with all available data (missing data were not imputed; AMOS 6.0; Arbuckle, 2005).

Factor analyses of ASD traits in children with and without ASD indicate the presence of social and restrictive — repetitive behaviour (RRB) factors.

Like other commonly used scales of nonspecific distress, the questions in the K10 / K6 scales all have high loadings on a first principal factor of nonspecific distress in factor analyses carried out in general population samples.8 This factor is indicated by a heterogeneous set of questions that define behavioral, emotional, cognitive, and psychophysiological manifestations of psychological distress.

Confirmatory factor analyses confirmed the theoretically hypothesized model in two independent German samples.

Principal component factor analyses revealed 2 factors that explained 50 % of the total variance: 1 factor for the 6 toy items and another factor with 2 items (books and cuddly toys).

Standardized Loadings from the Confirmatory Factor Analyses, Item Means, and Subscale Internal Consistency Coefficients for Study 1 and Study 3

Factor analyses of autistic traits in clinical ASD and community samples, using a variety of ASD measurement tools, generally indicate that multiple factors account for the observed covariance structure of ASD symptoms and traits (Happé and Ronald 2008; Mandy and Skuse 2008).

We validated the sample construction and examined the relationship between ACE items and total ACE scores (dependent variables) and important independent variables using polychoric factor analyses (designed for binary data) of the ACE survey items» factor structure.

The statistical analyses were performed using STATA 13.1 (StataCorp, College Station, TX), and the confirmatory factor analyses were performed using LISREL 8.8.

Note: Once again, thanks to Justin Bender and Nathan Stretch for their help with these factor analyses.

Were there any significant alphas observed in the factor analyses?

We conducted factor analyses for a number of additional items related to district initiatives for improvement.

My recent work includes a 12 - nation cross-cultural study and factor analyses studies of 20,000 North American adults and teenagers.

Several factor analyses of the responses revealed some clustering around religious, social, and individualistic ideas, but the results also suggested a high degree of «mixing» among different thematic traditions.

The DEA will now conduct its own eight - factor analysis to study the drug's potential for abuse, the current state of medical and scientific knowledge, the history and pattern of abuse, and other considerations.

Risk factor analysis shows that equity market sectors that act like «bond proxies» may be more sensitive to changes in interest rates than bonds themselves.