Empirical studies in psychology commonly report Cronbach's alpha as a measure of internal consistency reliability despite the fact that many methodological studies have shown that Cronbach's alpha is riddled with problems stemming from unrealistic assumptions. In many circumstances, violating these assumptions yields estimates of reliability that are too small, making measures look less reliable than they actually are. Although methodological critiques of Cronbach's alpha are being cited with increasing frequency in empirical studies, in this tutorial we discuss how the trend is not necessarily improving methodology used in the literature. That is, many studies continue to use Cronbach's alpha without regard for its assumptions or merely cite methodological articles advising against its use to rationalize unfavorable Cronbach's alpha estimates. This tutorial first provides evidence that recommendations against Cronbach's alpha have not appreciably changed how empirical studies report reliability. Then, we summarize the drawbacks of Cronbach's alpha conceptually without relying on mathematical or simulation-based arguments so that these arguments are accessible to a broad audience. We continue by discussing several alternative measures that make less rigid assumptions which provide justifiably higher estimates of reliability compared to Cronbach's alpha. We conclude with empirical examples to illustrate advantages of alternative measures of reliability including omega total, Revelle's omega total, the greatest lower bound, and Coefficient H. A detailed software appendix is also provided to help researchers implement alternative methods. (PsycINFO Database Record

Thanks coefficient alpha, we'll take it from here.

Bifactor measurement models are increasingly being applied to personality and psychopathology measures (Reise, 2012). In this work, authors generally have emphasized model fit, and their typical conclusion is that a bifactor model provides a superior fit relative to alternative subordinate models. Often unexplored, however, are important statistical indices that can substantially improve the psychometric analysis of a measure. We provide a review of the particularly valuable statistical indices one can derive from bifactor models. They include omega reliability coefficients, factor determinacy, construct reliability, explained common variance, and percentage of uncontaminated correlations. We describe how these indices can be calculated and used to inform: (a) the quality of unit-weighted total and subscale score composites, as well as factor score estimates, and (b) the specification and quality of a measurement model in structural equation modeling. (PsycINFO Database Record

https://psycnet.apa.org/journals/met/21/2/137.pdf

Evaluating bifactor models: Calculating and interpreting statistical indices.

[What is a cost-benefit analysis?].

A Philosophical Enquiry into the Origin of Our Ideas of the Sublime and Beautiful

Cronbach’s alpha (α) is a widely-used measure of reliability used to quantify the amount of random measurement error that exists in a sum score or average generated by a multi-item measurement scal...

Use Omega Rather than Cronbach’s Alpha for Estimating Reliability. But…

Coefficient alpha is the most popular measure of reliability (and certainly of internal consistency reliability) reported in psychological research. This is noteworthy given the numerous deficiencies of coefficient alpha documented in the psychometric literature. This mismatch between theory and practice appears to arise partly because users of psychological scales are unfamiliar with the psychometric literature on coefficient alpha and partly because alternatives to alpha are not widely known. We present a brief review of the psychometric literature on coefficient alpha, followed by a practical alternative in the form of coefficient omega. To facilitate the shift from alpha to omega, we also present a brief guide to the calculation of point and interval estimates of omega using a free, open source software environment.

/pdf/from-alpha-to-omega-a-practical-solution-to-the-pervasive-4m4zcidq56.pdf

From alpha to omega: a practical solution to the pervasive problem of internal consistency estimation

One of the more prominent issues in the field of Internet addiction is the validity of the instrument used to assess users' level of Internet involvement. Many of the instruments used to assess Internet addiction have high face validity but have yet to be tested psychometrically. The aim of this study is to compare two of the most used Internet addiction research measures, the Internet Addiction Test (IAT) and the Internet-Related Problem Scale (IRPS), along with a self-diagnostic question simply asking Internet users if they thought they were addicted to the Internet. A total of 225 Internet users participated in the study (69 males and 156 females). Participants who defined themselves as Internet addicts had higher scores on both the IAT and IRPS, and the three different Internet addiction measures were strongly correlated to each other. For the IAT, factor analysis generated three factors (emotional/psychological conflict; time management issues; mood modification) explaining 56.3% of the vari...

/pdf/a-psychometric-comparison-of-the-internet-addiction-test-the-3mmg0eyn7j.pdf

A Psychometric Comparison of the Internet Addiction Test, the Internet-Related Problem Scale, and Self-Diagnosis

This study examined Tinto's (1975) model of student attrition, which suggests that student's dropout decisions are social and academic worlds. The model was tested with path analysis using LISREL8 software (Joreskog & Sorbom, 1993a) using maximum likelihood estimation. Findings were that the model does not provide an acceptable description of the data. The findings were based on a global test of the model as a whole, as well as tests of individual paths within the model. Prior investigations into the model have been largely supportive, and reasons are considered for the disparity between previous and current findings. It is suggested that Tinto's perspective may not be the most appropriate for attrition research. Instead it is contended that interactionist and ethnographic approaches may result in a more appropriate theoretical framework.

Why do HE Students Drop Out? A test of Tinto's model

Satisfaction With Life Scale: analysis of factorial invariance, mean structures and reliability

Armstrong et al. (International Journal of Human Computer Studies, 53:537–550, 2000) explored the relationship between problematic Internet use and self-esteem. They devised a questionnaire called the Internet Related Problem Scale (IRPS) in order to measure the level of problem an individual is having due to their Internet use. This questionnaire study further examined the psychometric properties of the IRPS. Participants were recruited through the Internet, yielding 79 valid responses (14 males and 65 females). Factor analysis of the IRPS revealed six factors—salience, negative effects, mood enhancement, productivity, loss of control, and lack of information. These factors showed good internal consistency and concurrent validity, with salience being the most reliable. The limitations to this study are also addressed. Despite these limitations, the findings do seem to suggest that the IRPS has good psychometric properties and can be used in future studies examining Internet addiction.

Vivienne Brunsden

Papers

From alpha to omega: a practical solution to the pervasive problem of internal consistency estimation

A Psychometric Comparison of the Internet Addiction Test, the Internet-Related Problem Scale, and Self-Diagnosis

Why do HE Students Drop Out? A test of Tinto's model

Satisfaction With Life Scale: analysis of factorial invariance, mean structures and reliability

The Psychometric Properties of the Internet Related Problem Scale: A Pilot Study