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Is coefficient alpha robust to non-normal data?Yanyan Sheng 1 * and Zhaohui Sheng 21Department of Educational Psychology and Special Education, Southern Illinois University, Carbondale, IL, USA2Department of Educational Leadership, Western Illinois University, Macomb, IL, USAORIGINAL RESEARCH ARTICLEpublished: 15 February 2012doi: 10.3389/fpsyg.2012.00034Edited by:Jason W. Osborne, Old DominionUniversity, USAReviewed by:Pamela Kaliski, College Board, USAJames Stamey, Baylor University,USA*Correspondence:Yanyan Sheng, Department ofEducational Psychology and SpecialEducation, Southern IllinoisUniversity, Wham 223, MC 4618,Carbondale, IL 62901-4618, USA.e-mail: ysheng@siu.eduCoefficient alpha has been a widely used measure by which internal consistency reliabilityis assessed. In addition to essential tau-equivalence and uncorrelated errors, normalityhas been noted as another important assumption for alpha. Earlier work on evaluating thisassumption considered either exclusively non-normal error score distributions, or limitedconditions. In view of this and the availability of advanced methods for generating univariatenon-normal data, Monte Carlo simulations were conducted to show that non-normal distributionsfor true or error scores do create problems for using alpha to estimate the internalconsistency reliability. The sample coefficient alpha is affected by leptokurtic true scoredistributions, or skewed and/or kurtotic error score distributions. Increased sample sizes,not test lengths, help improve the accuracy, bias, or precision of using it with non-normaldata.Keywords: coefficient alpha, true score distribution, error score distribution, non-normality, skew, kurtosis, MonteCarlo, power method polynomialsINTRODUCTIONCoefficient alpha (Guttman, 1945; Cronbach, 1951) has been oneof the most commonly used measures today to assess internalconsistency reliability despite criticisms of its use (e.g., Raykov,1998; Green and Hershberger,2000; Green andYang,2009; Sijtsma,2009). The derivation of the coefficient is based on classical testtheory (CTT; Lord and Novick, 1968), which posits that a person’sobserved score is a linear function of his/her unobserved true score(or underlying construct) and error score. In the theory, measurescan be parallel (essential) tau-equivalent, or congeneric, dependingon the assumptions on the units of measurement, degrees ofprecision, and/or error variances. When two tests are designed tomeasure the same latent construct, they are parallel if they measureit with identical units of measurement, the same precision,and the same amounts of error; tau-equivalent if they measure itwith the same units, the same precision, but have possibly differenterror variance; essentially tau-equivalent if they assess it usingthe same units, but with possibly different precision and differentamounts of error; or congeneric if they assess it with possiblydifferent units of measurement, precision, and amounts of error(Lord and Novick, 1968; Graham, 2006). From parallel to congeneric,tests are requiring less strict assumptions and hence arebecoming more general. Studies (Lord and Novick, 1968, pp. 87–91; see also Novick and Lewis, 1967, pp. 6–7) have shown formallythat the population coefficient alpha equals internal consistencyreliability for tests that are tau-equivalent or at least essential tauequivalent.It underestimates the actual reliability for the moregeneral congeneric test. Apart from essential tau-equivalence,coefficientalpha requires two additional assumptions: uncorrelatederrors (Guttman, 1945; Novick and Lewis, 1967) and normality(e.g., Zumbo, 1999). Over the past decades, studies have well documentedthe effects of violations of essential tau-equivalence anduncorrelated errors (e.g., Zimmerman et al., 1993; Miller, 1995;Raykov, 1998; Green and Hershberger, 2000; Zumbo and Rupp,2004; Graham, 2006; Green and Yang, 2009), which have beenconsidered as two major assumptions for alpha. The normalityassumption, however, has received little attention. This could bea concern in typical applications where the population coefficientis an unknown parameter and has to be estimated usingthe sample coefficient. When data are normally distributed, samplecoefficient alpha has been shown to be an unbiased estimateof the population coefficient alpha (Kristof, 1963; van Zyl et al.,2000); however, less is known about situations when data arenon-normal.Over the past decades, the effect of departure from normalityon the sample coefficient alpha has been evaluated by Bay (1973),Shultz (1993), and Zimmerman et al. (1993) using Monte Carlosimulations. They reached different conclusions on the effect ofnon-normal data. In particular, Bay (1973) concluded that a leptokurtictrue score distribution could cause coefficient alpha toseriously underestimate internal consistency reliability. Zimmermanet al. (1993) and Shultz (1993), on the other hand, foundthat the sample coefficient alpha was fairly robust to departurefrom the normality assumption. The three studies differed in thedesign, in the factors manipulated and in the non-normal distributionsconsidered, but each is limited in certain ways. For example,Zimmerman et al. (1993) and Shultz (1993) only evaluated theeffect of non-normal error score distributions. Bay (1973), whilelooked at the effect of non-normal true score or error score distributions,only studied conditions of 30 subjects and 8 test items.Moreover, these studies have considered only two or three scenarioswhen it comes to non-normal distributions. Specifically, Bay(1973) employed uniform (symmetric platykurtic) and exponential(non-symmetric leptokurtic with positive skew) distributionsfor both true and error scores. Zimmerman et al. (1993) generatederror scores from uniform, exponential, and mixed normal (symmetricleptokurtic) distributions, while Shultz (1993) generatedthem using exponential, mixed normal, and negative exponentialwww.frontiersin.org February 2012 | Volume 3 | Article 34 | 28