Applied Meta-Analysis for Social Science Research

By Noel A. Card. 2011

1,101 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6d73039382ecdeb071737875e51881038b70b1c9

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Citations in the corpus

The Triple P-Positive Parenting Program: a systematic review and meta-analysis of a multi-level system of parenting support. (2014, background): “While asymmetry in funnel plots indicates a tendency for smaller studies to have larger effect sizes, there is no mechanism to determine causality (Card, 2012).”
Determinants of Entrepreneurial Intent: A Meta–Analytic Test and Integration of Competing Models (2012, background): “In the case of an insufficient number of studies to conduct the moderator analysis (k < 10), the respective effect size relationship was excluded from the moderator analysis (Card, 2012).”
The psychometric assessment of human life history strategy: A meta-analytic construct validation. (2014, background): “These special precautions were required because we decided to metaanalytically aggregate specific individual indicators across studies before we aggregated multiple convergent indicators across studies to produce our multivariate latent constructs (Card, 2011).”
The religious correlates of death anxiety: a systematic review and meta-analysis (2018, unknown): “When other measures of the association were reported (mean differences, t-test, odds ratio, χ2, etc.) we transformed them to r (see Card, 2011; Rosenthal, Rosnow, & Rubin, 2000).”
A Meta–Analytic and Conceptual Update on the Associations between Procrastination and Multidimensional Perfectionism (2017, methodology): “For each of these studies, the multiple raw effect sizes were averaged to form a single effect size, as this is a common approach for handling this issue (Card, 2012).”
Less Adaptive or More Maladaptive? A Meta–analytic Investigation of Procrastination and Coping (2015, methodology): “…sizes was assessed with two methods: (1) the heterogeneity statistic, Q, which assessed the degree of variability among the pool of effects sizes (Card, 2012) and (2) the I2 statistic (Slosar, 2009), which estimated the proportion of variability present in the effect sizes that is not due to…”
Creative and cultural work without filters: Covid-19 and exposed precarity in the creative economy (2020, methodology): “In part two, we introduce our methodological perspective and consider the value of adopting a meta-analytical framework (Card, 2015) to reflect on the current knowledge sought (and found) in relation to CCW and Covid-19.”
Self-Compassion and Coping: a Meta-Analysis (2021, result): “Furthermore, random-effects models consider the variability in study effect sizes due to the population variability in effect sizes (Card, 2012), which allows for generalization of the results beyond the set of studies analyzed in the meta-analysis (Hedges and Vevea 1998).”
Treatment for School Refusal Among Children and Adolescents (2018, unknown): “Because there were fewer than 10 studies in this review, the use of funnel plots and other statistical techniques to assess publication bias was not warranted (Card, 2011); therefore, we examined publication status as a potential moderator.”
Mathematical Interventions for Secondary Students With Learning Disabilities and Mathematics Difficulties: A Meta-Analysis (2018, methodology): “We selected a random effects model to analyze the effect sizes and compute estimates of mean effects and standard errors, as this model allows for generalizations to be made beyond the studies included in the analysis to the population of studies from which they come (Card, 2012).”
The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education (2019, background): “For the studies that reported correlations based on latent variables, there is no need for un-reliability corrections, because factor correlations are free from measurement error (Card, 2015).”
Generational Differences in Academic Achievement Among Immigrant Youths (2016, unknown): “Additionally, our inclusion of unpublished manuscripts and our use of random effects analyses allows for greater generalizability of findings compared to a sole reliance on published data and fixed effects models (Card, 2012).”
Individual Differences in Reading Development (2014, unknown): “In a next step, the individual effect sizes respectively correlations were transformed using Fisher’s transformation formula (Card, 2012; Lipsey & Wilson, 2001).”