With increasing numbers of studies on research ethics and a need to improve the recruitment of research subjects, the ability to measure attitudes toward biomedical research has become important. The Research Attitudes Questionnaire is a significant predictor of the public's attitudes toward and willingness to participate in research, yet limited data are available on its psychometric properties. This study establishes the scale's internal consistency and dimensionality using a large Internet-based sample from the United States. One item was removed due to a poor item-total correlation, and three additional items were removed which formed a reverse-wording measurement artifact factor. With improved internal consistency and dimensionality, the seven-item version has the advantages of shorter administration time and improved psychometric properties.
BentlerP. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.
3.
BriggsN. & MacCallumR. (2003). Recovery of weak common factors by maximum likelihood and ordinary least squares estimation. Multivariate Behavioral Research, 38(1), 25–56.
4.
BrowneM. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36(1), 111–150.
5.
BrowneM. & CudeckR. (1993). Alternative ways of assessing model fit. In BollenK. A. & LongJ. S. (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.
6.
CattellR. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276.
7.
CronbachL. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
8.
CudeckR. (2000). Exploratory factor analysis. In TinsleyH. & BrownS. (Eds.), Handbook of applied multivariate statistics and mathematical modeling (pp. 265–296). New York: Academic Press.
9.
FabrigarL.WegenerD.MacCallumR., & StrahanE. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272–299.
10.
GlorfeldL. (1995). An improvement on Horn's parallel analysis methodology for selecting the correct number of factors to retain. Educational and Psychological Measurement, 55, 377–393.
11.
GoldbergL. & VelicerW. (2006). Principles of exploratory factor analysis. In StrackS. (Ed.), Differentiating normal and abnormal personality (pp. 209–337). New York: Springer.
GorsuchR. (2003). Factor analysis. In ShinkaJ. & VelicerF. (Eds.), Handbook of psychology: Vol. 2, Research methods in psychology (pp. 143–164). Hoboken, NJ: John Wiley.
14.
HornJ. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185.
KaiserH. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141–151.
17.
KaiserH. (1974). An index of factorial simplicity. Psychometrika, 39, 31–36.
18.
KarlawishJ.CaryM.RubrightJ., & TenHaveT. (2008). How redesigning AD clinical trials might increase study partners' willingness to participate. Neurology, 71(23), 1883–1888.
19.
KarlawishJ.CasarettD.KlocinskiJ., & SankarP. (2001). How do AD patients and their caregivers decide whether to enroll in a clinical trial?Neurology, 56(6), 789–792.
20.
KarlawishJ.RubrightJ.CasarettD.CaryM.TenHaveT., & SankarP. (2009). Older adults' attitudes toward noncompetent subjects participating in Alzheimers research. American Journal of Psychiatry, 166(2), 182–188.
21.
KimS.KimH.McCallumC., & TariotP. (2005). What do people at risk for Alzheimer disease think about surrogate consent for research?Neurology, 65(9), 1395–1401.
22.
KlineP. (1994). An easy guide to factor analysis. New York: Routledge.
23.
MuroffJ.HoeraufS., & KimS. (2006). Is psychiatric research stigmatized? An experimental survey of the public. Schizophrenia Bulletin, 32(1), 129–136.
24.
SAS (Version 9.1). Cary, NC: SAS Institute, Inc.
25.
SugarmanJ.KassN.GoodmanS.PerentesisP.FernandesP., & FadenR. (1998). What patients say about medical research. IRB: A Review of Human Subjects Research, 20(4), 1–7.
26.
TatarynD.WoodJ., & GorsuchR. (1999). Setting the value of k in promax: A Monte Carlo study. Educational and Psychological Measurement, 59, 384–391.
VelicerW. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41, 321–327.
29.
VelicerW.EatonC., & FavaJ. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In GoffinR. & HelmsE. (Eds.), Problems and solutions in human assessment: Honoring Douglas N. Jackson at seventy (pp. 41–71). New York: Guilford.
30.
ZwickW. & VelicerW. (1986). Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99, 432–442.
