Reliability Generalization of the Psychopathy Checklist Applied in Youthful Samples

Campbell, Justin S.; Pulos, Steven; Hogan, Mike; Murry, Francie R.

doi:10.1177/0013164405275666

Cited by 17 publications

(22 citation statements)

References 54 publications

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“…Of the variables coded, only scale score standard deviation reached statistical significance. This finding has been noted in other RG studies including Campbell, Pulos, Hogan, and Murry (2005); Henson, Kogan, and Vacha-Haase (2001); Ryngala, Shields, and Caruso (2005); Vassar (2008); Vassar and Crosby (2008); and Vassar and Bradley (2010). In some instances, score variance has been the greatest predictor of variability in reliability coefficients (Caruso, Witkiewitz, Gottlieb, & Belcourt-Dittloff, 2001; Ryngala, Shields, & Caruso, 2005).…”

Section: Discussionsupporting

confidence: 80%

A reliability generalization meta-analysis of coefficient alpha for the Reynolds Adolescent Depression Scale

Vassar

Bradley

2011

Clin Child Psychol Psychiatry

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The purpose of this study was to use a meta-analytic method known as reliability generalization to investigate the score reliability for a popular depression measure: The Reynolds Adolescent Depression Scale. We used the technique to provide an aggregate estimate of coefficient alpha across empirical studies that have employed the measure over time and across populations. Furthermore, we identified sample and demographic characteristics associated with variance in coefficient alpha. We discuss conditions associated with variability in coefficient alpha and alert researchers and practitioners to appropriate uses of the scale based on common reliability benchmarks.

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Section: Discussionsupporting

confidence: 80%

A reliability generalization meta-analysis of coefficient alpha for the Reynolds Adolescent Depression Scale

Vassar

Bradley

2011

Clin Child Psychol Psychiatry

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“…To date, several dozen RG meta-analyses have been reported across an impressive array of measures. For example, RG studies have been conducted on literatures for measures involving state-trait anxiety (Barnes, Harp, & Jung, 2002), locus of control (Beretvas, Suizzo, Durham, & Yarnell, 2008), mathematics anxiety (Capraro, Capraro, & Henson, 2001), psychopathology (Campbell, Pulos, Hogan, & Murry, 2005), learning styles (Henson & Hwang, 2002), substance abuse propensities (Miller, Woodson, Howell, & Shields, 2009), ways of coping (Rexrode, Petersen, & O'Toole, 2008), and life satisfaction (Wallace & Wheeler, 2002).…”

Section: Reliability Generalization (Rg) Meta-analysismentioning

confidence: 99%

Score Reliability: A Retrospective Look Back at 12 Years of Reliability Generalization Studies

Vacha‐Haase

Thompson

2011

Measurement and Evaluation in Counseling and Development

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All the statistical analyses (e.g., t tests, ANOVA, ANCOVA, Pearson r, regression, as well as T 2 , MANOVA, MANCOVA, descriptive discriminant analysis, canonical correlation analysis) within the general linear model (GLM; see Cohen, 1968; Knapp, 1978) are correlational in that the implicit building block for these analyses is the computation of the intervariable correlation or covariance matrix. Indeed, secondary analyses of previously published results are easily performed given access to these matrices, even if the raw data are unavailable (Zientek & Thompson, 2009). However, poor score reliability will compromise estimates of both statistical significance (i.e., p CALCULATED values) and effect size within classical GLM analyses, because score reliabilities are not considered by the analyses. Instead, classical GLM analyses assume perfect or at least very good score reliabilities. Score reliability characterizes the degree to which scores measure "something" as opposed to "nothing" (e.g., are completely random). Random variations in data, including the random variations associated with measurement error, attenuate the relationships among measured variables. Such attenuation occurs because correlation coefficients are sensitive to systematic covariances among measured variables replicated over study participants and not random fluctuations. The fact that poor score reliability compromises the foundation of commonly applied statistical analyses suggests the obvious conclusion that evaluation of the score reliabilities for the scores in hand ought to be the 409845M ECXXX10.

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“…As coefficients alpha have a non‐normal distribution, RE model proponents recommend applying some transformation, such as the Fisher z ‐transform (Campbell, Pulos, Hogan, & Murry, ), Hakstian–Whalen transformation (Aguayo, Vargas, de la Fuente, & Lozano, ), or Bonett's transformation (Rendina‐Gobioff, ).…”

Section: Statistical Methods In Rg Studiesmentioning

confidence: 99%

Some recommended statistical analytic practices when reliability generalization studies are conducted

Meca

López-López

López‐Pina

2012

Brit J Math & Statis

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Precursors of the reliability generalization (RG) meta-analytic approach have not established a single preferred analytic method. By means of five real RG examples, we examine how using different statistical methods to integrate coefficients alpha can influence results in RG studies. Specifically, we compare thirteen different statistical models for averaging reliability coefficients and searching for moderator variables that differ in terms of: (a) whether to transform or not the coefficients alpha, and (b) the statistical model assumed, distinguishing between ordinary least squares methods, the fixed-effect (FE) model, the varying coefficient (VC) model, and several versions of the random-effects (RE) model. The results obtained with the different methods exhibited important discrepancies, especially regarding moderator analyses. The main criterion for the model choice should be the extent to which the meta-analyst intends to generalize the results. RE models are the most appropriate when the meta-analyst aims to generalize to a hypothetical population of past or future studies, while FE and VC models are the most appropriate when the interest focuses on generalizing the results to a population of studies identical to those included in the meta-analysis. Finally, some guidelines are proposed for selecting the statistical model when conducting an RG study.

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Reliability Generalization of the Psychopathy Checklist Applied in Youthful Samples

Cited by 17 publications

References 54 publications

A reliability generalization meta-analysis of coefficient alpha for the Reynolds Adolescent Depression Scale

A reliability generalization meta-analysis of coefficient alpha for the Reynolds Adolescent Depression Scale

Score Reliability: A Retrospective Look Back at 12 Years of Reliability Generalization Studies

Some recommended statistical analytic practices when reliability generalization studies are conducted

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