Asymptotically distribution-free (ADF) interval estimation of coefficient alpha.

Maydeu-Olivares, Alberto; Coffman, Donna L.; Hartmann, Wolfgang M.

doi:10.1037/1082-989x.12.2.157

Cited by 97 publications

(121 citation statements)

References 27 publications

Supporting

Mentioning

115

Contrasting

Order By: Relevance

“…Theoretically, ordinal alpha equals the true reliability when the items are tau-equivalent and fit a one-factor model with equal factor pattern coefficients (Maydeu-Olivares et al, 2007;Zumbo et al, 2007).…”

Section: Ordinal Coefficient Alphamentioning

confidence: 99%

“…The sample sizes were selected based on previous studies and represent sample sizes often noted in applied research (Cui & Li, 2012;Duhachek & Iacobucci, 2004;Maydeu-Olivares et al, 2007; Padilla et al, 2012; Romano et al, 2011; Yuan & Bentler, 2002;Zumbo et al, 2007). While EVALUATION OF CI FOR ORDINAL COEFFICIENT ALPHA 166 large sample sizes are always desirable, they are not always realistic.…”

Section: Sample Size (N)mentioning

confidence: 99%

“…Nevertheless, researchers often treat ordinally-scaled data as continuous by using statistical methods that assume continuity of data. This causes an empirical mismatch with the data analyzed (Gadermann, Guhn, & Zumbo, 2012;Schmitt, 1996;Sijtsma, 2009;Streiner, 2003), underestimation of sample coefficient alpha, and may lead researchers to incorrect conclusions (Duhachek & Iacobucci, 2004;Flora & Curran, 2004;Gadermann et al, 2012;Maydeu-Olivares, Coffman, & Hartmann, 2007;Zumbo, Gadermann, & Zeisser, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…This finding was consistent across all study conditions, but their study was not generalizable to Likert-type data. Maydeu-Olivares et al (2007) found that the empirical coverage rate of the ADF intervals for coefficient alpha outperforms that of normal theory intervals, regardless of observed skewness and kurtosis of item distributions (Cui & Li, 2012; Romano et al, 2011). These results are significant because researchers are no longer bound by normality assumptions (i.e.…”

mentioning

confidence: 99%

“…The code was executed in a Windows-based environment (Version 8). Based on Maydeu-Olivares et al (2007) and Hakstian TURNER ET AL. …”

mentioning

confidence: 99%

See 4 more Smart Citations

Vol. 16, No. 2 (Full Issue)

2017

J. Mod. App. Stat. Meth.

View full text Add to dashboard Cite

EDITORIAL ASSISTANCEJMASM (ISSN 1538−9472, http://digitalcommons.wayne.edu/jmasm) is an independent, open access electronic journal, published biannually in May and November by JMASM Inc. (PO Box 48023, Oak Park, MI, 48237) in collaboration with the Wayne State University Library System. JMASM seeks to publish (1) new statistical tests or procedures, or the comparison of existing statistical tests or procedures, using computer-intensive Monte Carlo, bootstrap, jackknife, or resampling methods, (2) the study of nonparametric, robust, permutation, exact, and approximate randomization methods, and (3) applications of computer programming, preferably in Fortran (all other programming environments are welcome), related to statistical algorithms, pseudo-random number generators, simulation techniques, and self-contained executable code to carry out new or interesting statistical methods.Journal correspondence (other than manuscript submissions) and requests for advertising may be forwarded to ea@jmasm.com. See back matter for instructions for authors. IntroductionComputer simulation studies represent an important tool for investigating statistical procedures difficult or impossible to study using mathematical theory or real data. Descriptors of these studies vary (e.g., statistical experiment, Monte Carlo simulation, computer experiment), but the examples of Hoaglin and Andrews (1975) and Hauck and Anderson (1984) are followed here with use of the term simulation studies. Extensive descriptions of simulation studies can be found in Lewis and Orav (1989) and Santner, Williams, and Notz (2003). In the behavioral sciences simulation studies have been used to study a wide array of statistical methods (e.g., Cribbie, Fiksenbaum, & Wilcox, 2012; Depaoli, 2012; Enders, Baraldi, & Cham, 2014; Hu & Bentler, 1999; Tomarken & Serlin, 1986). The general goal of these studies is to provide evidence of the behavior of statistical methods under a variety of data conditions that improves statistical practice and informs future statistical research. The goal here is to encourage methodological researchers to treat these studies as statistical sampling EXPERIMENTAL DESIGN AND DATA ANALYSIS IN SIMULATION 4 experiments subject to established principles of experimental design and data analysis.An underappreciated facet of simulation studies in statistics is their role in enhancing the reproducibility of scientific findings. The importance of reproducibility has gained momentum in numerous scientific arenas because of growing evidence that many findings cannot be replicated (Stodden, 2015). Concerns over reproducibility and the role of statistics were captured in Statistics and science: A report of the London workshop on the future of the statistical sciences (2014) which noted: "The reproducibility problem goes far beyond statistics, of course, because it involves the entire reward structure of the scientific enterprise. Nevertheless, statistics is a very important ingredient in both the problem and the remedy." (p. 27) Simulati...

show abstract

Section: Ordinal Coefficient Alphamentioning

confidence: 99%

Section: Sample Size (N)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…The code was executed in a Windows-based environment (Version 8). Based on Maydeu-Olivares et al (2007) and Hakstian TURNER ET AL. …”

mentioning

confidence: 99%

See 3 more Smart Citations

Vol. 16, No. 2 (Full Issue)

2017

J. Mod. App. Stat. Meth.

View full text Add to dashboard Cite

show abstract

Structural Equation Modeling

Ullman

Bentler

2012

Handbook of Psychology, Second Edition

295

148

View full text Add to dashboard Cite

The alpha and omega of financial risk‐tolerance assessment

et al. 2022

View full text Add to dashboard Cite

Over the past three decades, numerous scaling and attitudinal measurement techniques have been developed to facilitate the assessment of an individual's financial risk tolerance. Cronbach's alpha has traditionally been used as the primary measure of scale reliability for assessment tools that have been developed using classical psychometric theory. Recently, however, psychometricians have raised concerns about the ongoing use of Cronbach's alpha as a robust measure of scale reliability. In its place, some have argued that reliability estimates should be based on greatest lower bound (GLB) and omega estimations.The purpose of this paper is to describe and compare these alternative reliability measures to Cronbach's alpha for a widely used research-focused financial risk-tolerance scale. Using a dataset with 179,450 observations, findings from this study suggest that while estimates based on Cronbach's alpha, omega, and the GLB do differ, for the most part, reliability estimates across the measures are more similar than dissimilar.

show abstract

Asymptotically distribution-free (ADF) interval estimation of coefficient alpha.

Cited by 97 publications

References 27 publications

Vol. 16, No. 2 (Full Issue)

Vol. 16, No. 2 (Full Issue)

Structural Equation Modeling

The alpha and omega of financial risk‐tolerance assessment

Contact Info

Product

Resources

About