The Bi-factor method

Holzinger, Karl J.; Swineford, Frances

doi:10.1007/bf02287965

Cited by 727 publications

(564 citation statements)

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“…Table 1 Items used in Study 1 (Huppert & So, 2013) fits the data better than the bifactor model (a), it would suggest that any systematic variance, not accounted for by the general factor, results from method effects rather than substantive factors. In both bifactor models all factors were uncorrelated (Holzinger & Swineford, 1937).…”

Section: Methodsmentioning

confidence: 99%

Support for a general factor of well-being

Longo

Coyne

Joseph

et al. 2016

Personality and Individual Differences

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Abstract. Well-being is typically defined as positive feeling (e.g. happiness), positive functioning (e.g. competence, meaning) or a combination of the two. Recent evidence indicates that well-being indicators belonging to different categories can be explained by single "general" factor of well-being (e.g. Jovanovic, 2015). We further test this hypothesis using a recent well-being scale, which includes indicators of positive feeling and positive functioning (Huppert & So, 2013). While the authors of the scale originally identified a two-factor structure, in view of recent evidence, we hypothesize that the two-factor solution may be due to a method effect of different items being measured with different rating scales. In study 1, we use data from the European Social Survey round 3 (n = 41,461) and find that two factors have poor discriminant validity and, after using a bifactor model to account for different rating scales, only the general factor is reliable. In study 2, we eliminate method effects by using the same rating scale across items, recruit a new sample (n = 507), and find that a onefactor model fits the data well. The results support the hypothesis that well-being indicators, typically categorized as "positive feeling" and "positive functioning," reflect a single general factor.

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Section: Methodsmentioning

confidence: 99%

Support for a general factor of well-being

Longo

Coyne

Joseph

et al. 2016

Personality and Individual Differences

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“…Sometimes, however, some systematic individual differences are common to a small set of items, which can be captured by specific factors, yielding the bifactor model. Holzinger and Swineford (1937) proposed the bifactor model in the context of classical intelligence theory. In this model, each observed variable depends on two factors: a general intelligence factor and a smaller factor characterizing a specific subset of the items.…”

Section: The Common Factor Modelmentioning

confidence: 99%

Random intercept item factor analysis.

Maydeu-Olivares

Coffman

2006

Psychological Methods

223

224

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The common factor model assumes that the linear coefficients (intercepts and factor loadings) linking the observed variables to the latent factors are fixed coefficients (i.e., common for all participants). When the observed variables are participants' observed responses to stimuli, such as their responses to the items of a questionnaire, the assumption of common linear coefficients may be too restrictive. For instance, this may occur if participants consistently use the response scale idiosyncratically. To account for this phenomenon, the authors partially relax the fixed coefficients assumption by allowing the intercepts in the factor model to change across participants. The model is attractive when m factors are expected on the basis of substantive theory but m ϩ 1 factors are needed in practice to adequately reproduce the data. Also, this model for single-level data can be fitted with conventional software for structural equation modeling. The authors demonstrate the use of this model with an empirical data set on optimism in which they compare it with competing models such as the bifactor and the correlated trait-correlated method minus 1 models.

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“…However, given the 17 symptoms are placed a priori into three separate criterion domains, we also considered modeling each symptom domain as its own severity dimension (i.e., a three factor model). Finally, we evaluated a bifactor model (Holzinger & Swineford, 1937;Yung, Thissen, & McLeod, 1999). In the bifactor model, each symptom is allowed to load on the primary dimension of PTSD and to the dimension underlying its assigned criterion.…”

Section: Data Analytic Planmentioning

confidence: 99%

“…local dependence). The bifactor model has been used extensively in cognitive domain analyses (Gustafsson & Balke, 1993;Holzinger & Swineford, 1937) and increasingly has been useful in describing relationships among psychopathological and personality constructs (Patrick, Hicks, Nichol, & Krueger, 2007;Reise, Morizot, & Hays, 2007). We used confirmatory factor analysis of tetrachoric correlations to specify each of the three models in mPlus (Muthen & Muthen, 1998.…”

Section: Data Analytic Planmentioning

confidence: 99%

Evaluating symptom expression as a function of a posttraumatic stress disorder severity

Palm

Strong

MacPherson

2009

Journal of Anxiety Disorders

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Little is known about the relative severity or typical sequence of Diagnostic and Statistical Manual (DSM-IV) symptoms of posttraumatic stress disorder (PTSD). Using data from the National Comorbidity Study -Replication , the current study used a logistic item response model to assess the degree to which DSM-IV symptoms combine to define a primary construct underlying PTSD, to identify which symptoms are associated with greater severity of PTSD, and to determine whether the symptoms and symptom patterns are influenced by gender. Results suggested that PTSD symptoms can be combined to assess a single dimension of PTSD severity, providing support for a continuum of symptom severity. However, several DSM-IV symptoms provided overlapping information, potentially reducing the effectiveness of these symptoms in describing a broad range of PTSD. More precise assessment of PTSD severity may help improve the descriptive value of PTSD measures relationship to continuous measures of treatment outcomes, and ultimately inform more effective treatments.

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The Bi-factor method

Cited by 727 publications

References 0 publications

Support for a general factor of well-being

Support for a general factor of well-being

Random intercept item factor analysis.

Evaluating symptom expression as a function of a posttraumatic stress disorder severity

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