Journal of Modern Applied Statistical Methods 

About: Journal of Modern Applied Statistical Methods is an academic journal published by Wayne State University Press. The journal publishes majorly in the area(s): Estimator & Sample size determination. It has an ISSN identifier of 1538-9472. It is also open access. Over the lifetime, 1081 publications have been published receiving 12667 citations. The journal is also known as: JMASM.

New Effect Size Rules of Thumb

Abstract: Recommendations to expand Cohen’s (1988) rules of thumb for interpreting effect sizes are given to include very small, very large, and huge effect sizes. The reasons for the expansion, and implications for designing Monte Carlo studies, are discussed.

Ordinal Versions of Coefficients Alpha and Theta for Likert Rating Scales

Abstract: Two new reliability indices, ordinal coefficient alpha and ordinal coefficient theta, are introduced. A simulation study was conducted in order to compare the new ordinal reliability estimates to each other and to coefficient alpha with Likert data. Results indicate that ordinal coefficients alpha and theta are consistently suitable estimates of the theoretical reliability, regardless of the magnitude of the theoretical reliability, the number of scale points, and the skewness of the scale point distributions. In contrast, coefficient alpha is in general a negatively biased estimate of reliability. The use of ordinal coefficients alpha and theta as alternatives to coefficient alpha when estimating the reliability based on Likert response items are recommended. The choice between the two ordinal coefficients depends on whether one is assuming a factor analysis model (ordinal coefficient alpha) or a principal components analysis model (ordinal coefficient theta).

Comparison of Model Fit Indices Used in Structural Equation Modeling Under Multivariate Normality

Abstract: The purpose of this study is to investigate the impact of estimation techniques and sample sizes on model fit indices in structural equation models constructed according to the number of exogenous latent variables under multivariate normality. The performances of fit indices are compared by considering effects of related factors. The Ratio Chi-square Test Statistic to Degree of Freedom, Root Mean Square Error of Approximation, and Comparative Fit Index are the least affected indices by estimation technique and sample size under multivariate normality, especially with large sample size.

Beta-Weibull Distribution: Some Properties and Applications to Censored Data

Abstract: Some properties of a four-parameter beta-Weibull distribution are discussed. The beta-Weibull distribution is shown to have bathtub, unimodal, increasing, and decreasing hazard functions. The distribution is applied to censored data sets on bus-motor failures and a censored data set on head-andneck-cancer clinical trial. A simulation is conducted to compare the beta-Weibull distribution with the exponentiated Weibull distribution.

Determining Parallel Analysis Criteria

Abstract: Exploratory factor analysis is an important analytic tool for investigating test validity. Of all the decisions made in exploratory factor analysis, determining the number of factors to extract is perhaps the most critical because incorrect specification will obscure the factor structure (Cattell, 1978; Glorfeld, 1995; Goodwin & Goodwin, 1999). Although overextraction might be somewhat less serious than under-extraction (Wood, Tataryn, & Gorsuch, 1996), it has been empirically demonstrated that both have deleterious effects (Fava & Velicer, 1992, 1996). Many criteria for determining the number of factors to extract have been proposed (Benson & Nasser, 1998). Unfortunately, most are inaccurate guides to practice (Kanyongo, 2005; Zwick & Velicer, 1986). Based upon current simulation research (Velicer, Eaton, & Fava, 2000; Zwick & Velicer, 1986), only two methods have consistently emerged as accurate: the Parallel Analysis (PA) method of Horn (1965) and the Minimum Average Partial (MAP) method of Velicer (1976).