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Showing 1 to 15 of 75 results Save | Export
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Richard S. Balkin; Quentin Hunter; Bradley T. Erford – Measurement and Evaluation in Counseling and Development, 2024
We describe best practices in reporting reliability estimates in counseling research with consideration to precision, generalization, and diverse populations. We provide a historical context to reporting reliability estimates, the limitations of past practices, and new methods to address reliability generalization. We highlight best practices…
Descriptors: Best Practices, Reliability, Counseling, Research
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van Aert, Robbie C. M.; Goos, Cas – Research Synthesis Methods, 2023
The partial correlation coefficient quantifies the relationship between two variables while taking into account the effect of one or multiple control variables. Researchers often want to synthesize partial correlation coefficients in a meta-analysis since these can be readily computed based on the reported results of a linear regression analysis.…
Descriptors: Computation, Sampling, Correlation, Meta Analysis
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Sean McGrath; XiaoFei Zhao; Omer Ozturk; Stephan Katzenschlager; Russell Steele; Andrea Benedetti – Research Synthesis Methods, 2024
When performing an aggregate data meta-analysis of a continuous outcome, researchers often come across primary studies that report the sample median of the outcome. However, standard meta-analytic methods typically cannot be directly applied in this setting. In recent years, there has been substantial development in statistical methods to…
Descriptors: Statistical Analysis, Meta Analysis, Data Analysis, Sampling
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van Aert, Robbie C. M. – Research Synthesis Methods, 2023
The partial correlation coefficient (PCC) is used to quantify the linear relationship between two variables while taking into account/controlling for other variables. Researchers frequently synthesize PCCs in a meta-analysis, but two of the assumptions of the common equal-effect and random-effects meta-analysis model are by definition violated.…
Descriptors: Correlation, Meta Analysis, Sampling, Simulation
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Winton, Bradley G.; Sabol, Misty A. – International Journal of Social Research Methodology, 2022
Convenience sampling dominates social science research. But there is a paucity of studies comparing the impact of sample source type based on composite-based theoretical model relationships. This study empirically tests four different sample sources (e.g. student, crowdsourced, professional panel, and respondent driven social network) to assess…
Descriptors: Sampling, Sample Size, Social Science Research, Measurement
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Brannick, Michael T.; French, Kimberly A.; Rothstein, Hannah R.; Kiselica, Andrew M.; Apostoloski, Nenad – Research Synthesis Methods, 2021
Tolerance intervals provide a bracket intended to contain a percentage (e.g., 80%) of a population distribution given sample estimates of the mean and variance. In random-effects meta-analysis, tolerance intervals should contain researcher-specified proportions of underlying population effect sizes. Using Monte Carlo simulation, we investigated…
Descriptors: Meta Analysis, Credibility, Intervals, Effect Size
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Noma, Hisashi; Hamura, Yasuyuki; Gosho, Masahiko; Furukawa, Toshi A. – Research Synthesis Methods, 2023
Network meta-analysis has been an essential methodology of systematic reviews for comparative effectiveness research. The restricted maximum likelihood (REML) method is one of the current standard inference methods for multivariate, contrast-based meta-analysis models, but recent studies have revealed the resultant confidence intervals of average…
Descriptors: Network Analysis, Meta Analysis, Regression (Statistics), Error of Measurement
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Domínguez Islas, Clara; Rice, Kenneth M. – Research Synthesis Methods, 2022
Bayesian methods seem a natural choice for combining sources of evidence in meta-analyses. However, in practice, their sensitivity to the choice of prior distribution is much less attractive, particularly for parameters describing heterogeneity. A recent non-Bayesian approach to fixed-effects meta-analysis provides novel ways to think about…
Descriptors: Bayesian Statistics, Evidence, Meta Analysis, Statistical Inference
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Schnell, Rainer; Thomas, Kathrin – Sociological Methods & Research, 2023
This article provides a meta-analysis of studies using the crosswise model (CM) in estimating the prevalence of sensitive characteristics in different samples and populations. On a data set of 141 items published in 33 either articles or books, we compare the difference ([delta]) between estimates based on the CM and a direct question (DQ). The…
Descriptors: Meta Analysis, Models, Comparative Analysis, Publications
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Stanley, T. D.; Doucouliagos, Hristos – Research Synthesis Methods, 2023
Partial correlation coefficients are often used as effect sizes in the meta-analysis and systematic review of multiple regression analysis research results. There are two well-known formulas for the variance and thereby for the standard error (SE) of partial correlation coefficients (PCC). One is considered the "correct" variance in the…
Descriptors: Correlation, Statistical Bias, Error Patterns, Error Correction
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Bom, Pedro R. D.; Rachinger, Heiko – Research Synthesis Methods, 2020
Meta-studies are often conducted on empirical findings obtained from overlapping samples. Sample overlap is common in research fields that strongly rely on aggregated observational data (eg, economics and finance), where the same set of data may be used in several studies. More generally, sample overlap tends to occur whenever multiple estimates…
Descriptors: Meta Analysis, Sampling, Research Problems, Computation
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Poom, Leo; af Wåhlberg, Anders – Research Synthesis Methods, 2022
In meta-analysis, effect sizes often need to be converted into a common metric. For this purpose conversion formulas have been constructed; some are exact, others are approximations whose accuracy has not yet been systematically tested. We performed Monte Carlo simulations where samples with pre-specified population correlations between the…
Descriptors: Meta Analysis, Effect Size, Mathematical Formulas, Monte Carlo Methods
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Weber, Frank; Knapp, Guido; Glass, Änne; Kundt, Günther; Ickstadt, Katja – Research Synthesis Methods, 2021
There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study…
Descriptors: Meta Analysis, Computation, Intervals, Statistical Analysis
Hedges, Larry V.; Schauer, Jacob M. – Grantee Submission, 2019
Formal empirical assessments of replication have recently become more prominent in several areas of science, including psychology. These assessments have used different statistical approaches to determine if a finding has been replicated. The purpose of this article is to provide several alternative conceptual frameworks that lead to different…
Descriptors: Statistical Analysis, Replication (Evaluation), Meta Analysis, Hypothesis Testing
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Dennis, Minyi Shih; Sorrells, Audrey M.; Chovanes, Jacquelyn; Kiru, Elisheba W. – Learning Disability Quarterly, 2022
This meta-analysis examined the ecological and population validity of intervention research for students with low mathematics achievement (SWLMA). Forty-four studies published between 2005 and 2019 that met the inclusionary criterion were included in this analysis. Our findings suggest, to improve the external validity and generalizability of…
Descriptors: Mathematics Achievement, Low Achievement, Intervention, Meta Analysis
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