ci.reliability(data = NULL, S = NULL, N = NULL, aux = NULL,
type = NULL, inttype = NULL, B = 1000, conf.level = 0.95)S is specified."alpha" or 1 for coefficient alpha analyzed by the formula proposed by Cronbach (1951), "alpha-cfa" or 2 for coefficient alpha analyzed by CFA with tdetails below. The default is to not provide any interval estimates. Based on our simulation studies (Kelley and Pornprasertmanit, in press), bias corrected and accelerated bootstrap, "bca"If researchers wish to make the measurement model with all parallel items (equal factor loadings and equal error variances), users can specify it by setting inttype = "parallel" and type = "alpha" or type = "alpha-cfa". See McDonald (1999) for the assumptions of each of these models.
The list below shows all methods to find the confidence interval of reliability.
"none"or0to not find any confidence interval"parallel"or11to assume that the items are parallel and analyze confidence interval based on wald confidence interval (see van Zyl, Neudecker, & Nel, 2000, Equation 22; also referred as the asymptotic method of Koning & Franses, 2003)."feldt"or12is based on that$\frac{1 - \alpha}{1 - \hat{alpha}}$is distributed as$F$distribution with the degree of freedoms of$N - 1$and$(N - 1) \times (p - 1)$(Feldt, 1965)."siotani"or13is the same as the"feldt"method but using the degree of freedoms of$N$and$N \times (p - 1)$(Siotani, Hayakawa, & Fujikoshi, 1985; van Zyl et al., 2000, Equations 7 and 8; also referred as the exact method of Koning & Franses, 2003)."fisher"or21for the Fisher's$z$transformation on the correlation coefficient approach,$z = 0.5 \times \log{\frac{1 + \alpha}{1 - \alpha}}$, directly on the coefficient alpha and find confidence interval of transformed scale (Fisher, 1950). The variance of the$z$is$\frac{1}{N - 3}$where$N$is the total sample size."bonett"or22for the Fisher's$z$transformation on the intraclass correlation approach with the variance of$\frac{2p}{(N - 2)(p - 1)}$(Bonett, 2002, Equation 6)."hakstian"or23uses the cube root transformation and assumes normal distribution on the cube root transformation (Hakstian & Whalen, 1976). The variance of the transformed reliability is based on the degrees of freedom in the"feldt"method."hakstianbarchard"or24uses a correction of the violation of compound symmetry of covariance matrix by adjusting the degrees of freedom in the"hakstian". This correction is used for the inference in type 12 sampling (both persons and items are sampled from the population of persons and items) See Hakstian and Barchard (2000) for further details."icc"or25for the Fisher's$z$transformation on the intraclass correlation approach,$z = \log{1 - \alpha}$. The variance of the$z$is$\frac{2p}{N(p - 1)}$where$p$is the number of items (Fisher, 1991, p. 221; van Zyl et al., 2000, p. 277)."ml"or31to analyze the confidence interval based on normal-theory approach (or multivariate delta method). See van Zyl, Neudecker, & Nel (2000, Equation 21) for the confidence interval of coefficient alpha (also be referred as Iacobucci & Duhachek's, 2003, method). See Raykov (2002) for details for coefficient omega. If users useanalytic.type="cfa", thesempackage will be used to obtain parameter estimates and standard errors used for the formula proposed by Raykov (2002)"mll"or32to analyze the confidence interval based on normal-theory approach as above. However, the point estimate and standard error were used to build confidence interval using logistic transformation as the note below."mlr"or33to analyze the confidence interval based on normal-theory approach (or multivariate delta method). However, the estimation method is to use robust standard error (Satorra and Bentler, 2000)."mlrl"or34to analyze he confidence interval based on normal-theory approach using robust standard error and logistic transformation (see below)."adf"or35for asymtotic distribution-free method (see Maydeu-Olivares, Coffman, & Hartman, 2007 for further details for coefficient omega; we use phantom variable approach, Cheung, 2009, and"WLS"estimator for coefficient omega, Browne, 1984, in thelavaanpackage, Rosseel, 2012)."adfl"or36to use asymptotic distribution-free method to derive standard error and parameter estimate. Then, logistic transformation is used to build confidence interval (see below)."ll"or37for profile likelihood-based confidence interval of both reliability coefficients (Cheung, 2009) analyzed by theOpenMxpackage (Boker et al., 2011)"bsi"or41for standard bootstrap confidence interval which find the standard deviation across the bootstrap estimates, multiply the standard deviation by critical value, and add and subtract from the reliability estimate."bsil"or42to use standard bootstrap confidence interval. However, logistic transformation is used to build confidence interval."perc"or43for percentile bootstrap confidence interval."bca"or44for bias-corrected and accelerated bootstrap confidence interval.The logistic transformation (Browne, 1982) is applicable for "ml", "mlr", "adf", and "bsi" as "mll", "mlrl", "adfl", and "bsil". The logistic transformation does not assume that the sampling distribution of reliability is symmetric. It acknowledges the fact that reliability ranges from 0 and 1. Logistic transformation is applied to the reliability estimates. Confidence interval is established for the transformed value. The lower and upper bounds of the transformed value is translated back to the reliability estimates. See Browne (1982) or Kelley and Pornprasertmanit (in press) for further details.
Note that not all confidence interval methods are available for all types of reliability and all types of input. For example, bootstrap confidence intervals are not available for covariance matrix input. Parallel confidence intervals are not available for hierarchical omega. We provided appropriate error messages for all impossible combinations.
Bonett, D. G. (2002). Sample size requirements for testing and estimating coefficient alpha. Journal of Educational and Behavioral Statistics, 27, 335-340.
Browne, M. W. (1982). Covariance structures. In D. M. Hawkins (Ed.), Topics in applied multivariate analysis (pp. 72-141). Cambridge, UK: Cambridge University Press.
Browne, M. W. (1984). Asymptotic distribution free methods in the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 24, 445-455.
Cheung, M. W.-L. (2009). Constructing approximate confidence intervals for parameters with structural constructing approximate confidence intervals for parameters with structural equation models. Structural Equation Modeling, 16, 267-294.
Feldt, L.S. (1965). The approximate sampling distribution of Kuder-Richardson reliability coefficient twenty. Psychometrika, 30, 357-370.
Fisher, R. A. (1950). Statistical methods for research workers. Edinburgh, UK: Oliver & Boyd.
Fisher, R. A. (1991). Statistical methods for research workers. In J.H. Bennett (Ed.), Statistical methods, experimental design, and scientific inference. Oxford: Oxford University Press.
Green, S. B., & Yang, Y. (2009). Reliability of summed item scores using structural equation modeling: An alternative to coefficient alpha. Psychometrika, 74, 155-167.
Hakstian, A. R., & Whalen, T. E. (1976). A k-sample significance test for independent alpha coefficients. Psychometrika, 41, 219-231.
Iacobucci, D., & Duhachek, A. (2003). Advancing alpha: measuring reliability with confidence. Journal of Consumer Psychology, 13, 478-487.
Kelley, K. & Pornprasertmanit, P. (in press). Confidence intervals for population reliability coefficients: Evaluation of methods, recommendations, and software for homogeneous composite measures. Psychological Methods.
Koning, A. J., & Franses, P. H. (2003). Confidence intervals for Cronbach`s coefficient alpha values (ERIM Report Series Ref. No. ERS-2003-041-MKT). Rotterdam, The Netherlands: Erasmus Research Institute of Management.
Maydeu-Olivares, A., Coffman, D. L., & Hartmann, W. M. (2007). Asymptotically distribution-free (ADF) interval estimation of coefficient alpha. Psychological Methods, 12, 157-176.
McDonald, R. P. (1999). Test theory: A unified approach. Mahwah, New Jersey: Lawrence Erlbaum Associates, Publishers.
Raykov, T. (2002). Analytic estimation of standard error and confidence interval for scale reliability. Multivariate Behavioral Research, 37, 89-103.
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1-36.
Satorra, A. & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66, 507-514.
Siotani, M., Hayakawa, T., & Fujikoshi, Y. (1985). Modem multivariate statistical analysis: A graduate course and handbook. Columbus, Ohio: American Sciences Press.
van Zyl, J. M., Neudecker, H., & Nel, D. G. (2000) On the distribution of the maximum likelihood estimator of Cronbach's alpha. Psychometrika, 65 (3), 271-280.
Yuan, K. & Bentler, P. M. (2002) On robustness of the normal-theory based asymptotic distributions of three reliability coefficient estimates. Psychometrika, 67 (2), 251-259.
# Use this function for the attitude dataset (ignoring the overall rating variable)
ci.reliability(data=attitude[,-1], type = "omega", inttype = "mlrl", B = 100)
## Forming a hypothetical population covariance matrix
Pop.Cov.Mat <- matrix(.3, 9, 9)
diag(Pop.Cov.Mat) <- 1
ci.reliability(S=Pop.Cov.Mat, N=50, type="alpha", inttype = "bonett")Run the code above in your browser using DataLab