Combining Reliability Coefficients: Possible Application to Meta-Analysis and Reliability Generalization

2003 ◽  
Vol 93 (3) ◽  
pp. 643-647 ◽  
Author(s):  
Richard A. Charter
Keyword(s):  
Meta Analysis ◽  
Coefficient Alpha ◽  
Sample Sizes ◽  
Reliability Generalization ◽  
Raw Data ◽  
Alternate Forms ◽  
Standard Deviations ◽  
Reliability Coefficients

Formulae for combining reliability coefficients from any number of samples are provided. These formulae produce the exact reliability one would compute if one had the raw data from the samples. Needed are the sample means, standard deviations, sample sizes, and reliability coefficients. The formulae work for coefficient alpha, KR-20, retest, alternate-forms, split-half, interrater (intraclass), Gilmer-Feldt, Angoff-Feldt, validity, and other coefficients. They may be particularly useful for meta-analytic and reliability generalization studies.

scholarly journals A Reliability Generalization Meta-Analysis of Coefficient Alpha for the Maslach Burnout Inventory

2011 ◽  
Vol 71 (1) ◽  
pp. 231-244 ◽  
Author(s):  
Denna L. Wheeler ◽  
Matt Vassar ◽  
Jody A. Worley ◽  
Laura L. B. Barnes
Keyword(s):  
Meta Analysis ◽  
Maslach Burnout Inventory ◽  
Coefficient Alpha ◽  
Reliability Generalization

scholarly journals Consequences of multiple imputation of missing standard deviations and sample sizes in meta‐analysis

2020 ◽  
Vol 10 (20) ◽  
pp. 11699-11712
Author(s):  
Stephan Kambach ◽  
Helge Bruelheide ◽  
Katharina Gerstner ◽  
Jessica Gurevitch ◽  
Michael Beckmann ◽  
...  
Keyword(s):  
Multiple Imputation ◽  
Meta Analysis ◽  
Sample Sizes ◽  
Standard Deviations

A reliability generalization meta-analysis of coefficient alpha and test–retest coefficient for the aging males’ symptoms (AMS) scale

2016 ◽  
Vol 19 (4) ◽  
pp. 244-253 ◽  
Author(s):  
Chin-Pang Lee ◽  
Yu-Wen Chiu ◽  
Chun-Lin Chu ◽  
Yu Chen ◽  
Kun-Hao Jiang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Coefficient Alpha ◽  
Reliability Generalization ◽  
Aging Males Symptoms

scholarly journals Consequences of multiple imputation of missing standard deviations and sample sizes in meta-analysis

2020 ◽  
Author(s):  
Stephan Kambach ◽  
Helge Bruelheide ◽  
Katharina Gerstner ◽  
Jessica Gurevitch ◽  
Michael Beckmann ◽  
...  
Keyword(s):  
Multiple Imputation ◽  
Meta Analysis ◽  
Sample Sizes ◽  
Standard Deviations

Meta-Analysis of Coefficient Alpha: A Reliability Generalization Study

2017 ◽  
Vol 55 (4) ◽  
pp. 583-618 ◽  
Author(s):  
Lindsey M. Greco ◽  
Ernest H. O'Boyle ◽  
Bethany S. Cockburn ◽  
Zhenyu Yuan
Keyword(s):  
Meta Analysis ◽  
Coefficient Alpha ◽  
Reliability Generalization

A Reliability Generalization of the Overexcitability Questionnaire–Two

2011 ◽  
Vol 22 (5) ◽  
pp. 671-692 ◽  
Author(s):  
Russell T. Warne
Keyword(s):  
United States ◽  
Sample Size ◽  
Meta Analysis ◽  
Gender Composition ◽  
Reliability Generalization ◽  
Cronbach’S Alpha ◽  
Sample Number ◽  
Cronbach's Alpha ◽  
Reported Data ◽  
Reliability Coefficients

Reliability generalization (RG) is a meta-analysis that combines and synthesizes reliability coefficients from different studies to ascertain the average observed reliability across studies. An RG study was conducted on previously reported data from 16 samples of the Overexcitability Questionnaire–Two (OEQII) with a combined N of 5,275. Cronbach’s alpha was found to be consistently higher on all OEQII subscales when scale variance was high and the sample consisted of adults. Sample size, gender composition of the sample, number of items from the subscale used, and location of sample (United States or a different county) had varying effects on observed alpha levels for each subscale. Suggestions are proposed for substantive research using the OEQII and for future psychometric research on the instrument.

Determinants of Interrater Reliability of Job Analysis: A Meta-analysis

2002 ◽  
Vol 18 (1) ◽  
pp. 52-62 ◽  
Author(s):  
Olga F. Voskuijl ◽  
Tjarda van Sliedregt
Keyword(s):  
Data Collection ◽  
Interrater Reliability ◽  
Meta Analysis ◽  
Job Analysis ◽  
Job Descriptions ◽  
Job Description ◽  
Research And Practice ◽  
Descriptive Information ◽  
Interrater Reliability Coefficients ◽  
Reliability Coefficients

Summary: This paper presents a meta-analysis of published job analysis interrater reliability data in order to predict the expected levels of interrater reliability within specific combinations of moderators, such as rater source, experience of the rater, and type of job descriptive information. The overall mean interrater reliability of 91 reliability coefficients reported in the literature was .59. The results of experienced professionals (job analysts) showed the highest reliability coefficients (.76). The method of data collection (job contact versus job description) only affected the results of experienced job analysts. For this group higher interrater reliability coefficients were obtained for analyses based on job contact (.87) than for those based on job descriptions (.71). For other rater categories (e.g., students, organization members) neither the method of data collection nor training had a significant effect on the interrater reliability. Analyses based on scales with defined levels resulted in significantly higher interrater reliability coefficients than analyses based on scales with undefined levels. Behavior and job worth dimensions were rated more reliable (.62 and .60, respectively) than attributes and tasks (.49 and .29, respectively). Furthermore, the results indicated that if nonprofessional raters are used (e.g., incumbents or students), at least two to four raters are required to obtain a reliability coefficient of .80. These findings have implications for research and practice.

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