title: The Beck Depression Inventory II factor structure among low-income women.
abstract: BACKGROUND
  The Beck Depression Inventory II (BDI-II) is considered a gold standard for identifying depression in adults. Validity of the BDI-II has been documented in diverse populations using exploratory factor analysis (EFA), although no findings have been reported exclusively among lower income women. Among EFA findings, the factor structure of the BDI-II has been inconsistent, with cognitive, affective, and somatic domains emerging differentially within factors across studies. This, in conjunction with concerns around the confounding of depressive symptoms as measured by the BDI-II and other illness states, has led researchers to examine more complex factor structures using confirmatory factor analysis (CFA).
  
  
  OBJECTIVE
  The purpose of this study was to evaluate the factor structure of the BDI-II using both EFA and CFA among low-income women.
  
  
  METHODS
  After EFA with Promax rotation, CFA testing was conducted on several structural models with two randomly split subsamples of 108 and 200 women going through a Welfare Transition Program.
  
  
  RESULTS
  A two-factor structure was indicated by EFA, with the cognitive and affective domains represented in Factor 1 and somatic items comprising Factor 2. CFA revealed a general factor model, with General Depression and residual Cognitive and Somatic factors, best fit to the data on the basis of several indices (root mean square error of approximation = 0.05; standardized root mean square residual = 0.05; weighted root mean square residual = 0.69; comparative fit index = .98; and Tucker-Lewis index = .99) and model difference tests of significance (four comparisons: all chi values >24.9, all p values < .001).
  
  
  CONCLUSIONS
  Measurement using BDI-II is best represented by a complex factor structure among low-income women and is consistent with findings in other populations. Additional consideration for how a general model factor structure provides potentially new directions for depression measurement may advance science in several areas.
authors.affiliation: Department of Healthcare Environments and Systems, College of Nursing, University of Florida, Gainesville, FL 32611, USA. skneipp@ufl.edu
authors.forename: Shawn M
authors.initials: SM
authors.lastname: Kneipp
authors.forename: John A
authors.initials: JA
authors.lastname: Kairalla
authors.forename: Jeanne
authors.initials: J
authors.lastname: Stacciarini
authors.forename: Deidre
authors.initials: D
authors.lastname: Pereira
mesh_terms: D000328:Adult; D003863:Depression / Q000175:diagnosis*; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D010555:Personality Inventory; D011203:Poverty; D011632:Public Assistance; D014930:Women / Q000523:psychology*