Exploratory Bi-Factor Analysis

Jennrich, Robert I.; Bentler, Peter M.

doi:10.1007/s11336-011-9218-4

Cited by 264 publications

(281 citation statements)

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“…As noted by Jennrich and Bentler (2011) bi-factor analysis is an extensively used form of confirmatory factor analysis. To use bi-factor analysis one must specify a specific bi-factor structure.…”

Section: Introductionmentioning

confidence: 99%

“…More recently Jennrich and Bentler (2011) introduced an exploratory form of bi-factor analysis. This does not require the specification of a specific 3 structure, but is designed to give a rotated loading matrix with an approximate bi-factor structure that may be used to suggest a specific structure.…”

Section: Introductionmentioning

confidence: 99%

“…As noted by Jennrich and Bentler (2011) the bi-factor model is more general than the two stage model on which the Schmid-Leiman method is based.…”

Section: Introductionmentioning

confidence: 99%

“…Since the bi-quartimin rotation criterion has already been defined in Jennrich and Bentler (2011) the only change required to move from orthogonal to oblique rotation is to use an oblique rotation algorithm rather than an orthogonal rotation algorithm. For the authors this meant changing one line of computer code.…”

mentioning

confidence: 99%

“…As noted above they used 10. Jennrich and Bentler (2011) show how to perform orthogonal bi-factor rotation.…”

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confidence: 99%

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Exploratory Bi-factor Analysis: The Oblique Case

2012

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Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford (1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler (2011) introduced an exploratory form of bi-factor analysis that does not require one to provide an explicit bi-factor structure a priori. They use exploratory factor analysis and a bi-factor rotation criterion designed to produce a rotated loading matrix that has an approximate bi-factor structure. Among other things this can be used as an aid in finding an explicit bi-factor structure for use in a confirmatory bi-factor analysis. They considered only orthogonal rotation. The purpose of this paper is to consider oblique rotation and to compare it to orthogonal rotation.Because there are many more oblique rotations of an initial loading matrix than orthogonal rotations, one expects the oblique results to approximate a bi-factor structure better than orthogonal rotations and this is indeed the case. A surprising 1 result arises when oblique bi-factor rotation methods are applied to ideal data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…As noted by Jennrich and Bentler (2011) the bi-factor model is more general than the two stage model on which the Schmid-Leiman method is based.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…As noted above they used 10. Jennrich and Bentler (2011) show how to perform orthogonal bi-factor rotation.…”

mentioning

confidence: 99%

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Exploratory Bi-factor Analysis: The Oblique Case

2012

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Association of Fetal Growth With General and Specific Mental Health Conditions

et al. 2019

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IMPORTANCE It is unclear if the associations between fetal growth and later mental health conditions remain after controlling for familial factors and psychiatric comorbidity. OBJECTIVE To examine the associations between fetal growth and general and specific mental health conditions, controlling for familial factors. DESIGN, SETTING, AND PARTICIPANTS This register-based study conducted in Sweden analyzed 546 894 pairs of full siblings born between January 1, 1973, and December 31, 1998. Sibling pairs were followed up through December 31, 2013. First, population-based and within-sibling pair associations (which controlled for time-invariant familial confounders) between fetal growth and the outcomes were estimated. Second, exploratory factor analysis was applied to the outcomes to derive 1 general factor and 4 specific and independent factors. Third, the general and specific factors were regressed on fetal growth. Statistical analysis was performed from March 27, 2017, to October 27, 2018. MAIN OUTCOME AND MEASURES The outcomes were 11 psychiatric diagnoses (depression, anxiety, obsessive-compulsive disorder, posttraumatic stress disorder, bipolar disorder, alcohol abuse, drug use, attention-deficit/hyperactivity disorder, autism, schizophrenia, and schizoaffective disorder) and court convictions of violent crimes. Birth weight (in kilograms) statistically adjusted for gestational age was the exposure. RESULTS The mean (SD) age of the 1 093 788 participants was 27.2 (6.8) years (range, 15.1-40.9 years) and 51.5% were male. Nine outcomes were significantly associated with birth weight in the population at large: depression (odds ratio [OR]

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