The Reviewer’s Guide to Quantitative Methods in the Social Sciences

By G. R. Hancock, R. Mueller, Laura M. Stapleton. 2018

880 citations recorded by the source corpus.

The Reviewer’s Guide to Quantitative Methods in the Social Sciences is designed for evaluators of research manuscripts and proposals in the social and behavioral sciences, and beyond. Its 31 uniquely structured chapters cover both traditional and emerging methods of quantitative data analysis, which neither junior nor veteran reviewers can be expected to know in detail. The book updates readers on each technique’s key principles, appropriate usage, underlying assumptions, and limitations. It thereby assists reviewers to offer constructive commentary on works they evaluate, and also serves as an indispensable author’s reference for preparing sound research manuscripts and proposals. Key features include:

Subfields: Education, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/76b698c03ef770ce0c63c2d89e8f76917843bf82

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Citations in the corpus

Self-Determination and Classroom Engagement of EFL Learners: A Mixed-Methods Study of the Self-System Model of Motivational Development (2019, methodology): “Good model fit was present when either the chisquare test was non-significant, or RMSEA and SRMR values were less than .08, and CFI was more than .95 (Bandalos & Finney, 2010; Hu & Bentler, 1999; Phakiti, 2018).”
Revisiting the Model Size Effect in Structural Equation Modeling (2018, methodology): “Many fit indexes are directly based on the chi-square statistic (e.g., CFI; Bentler, 1990) and have been routinely reported in SEM applications as standard tools for evaluating model fit (McDonald & Ho, 2002; Mueller & Hancock, 2010).”
Multilevel and Single-Level Models for Measured and Latent Variables When Data Are Clustered (2016, methodology): “Each of these assumptions and modeling decisions is crucial (McCoach, 2010), so the applied researcher, prior to settling on a multilevel analysis, should consider the goal of the analysis and, if a multilevel model is warranted, address the many modeling decisions appropriately.”
The Relationship Between Teachers’ Implementation of Classroom Management Practices and Student Behavior in Elementary School (2018, methodology): “We confirmed fit using a combination of entropy, classification error, and, as suggested by Samuelsen and Dayton (2010), the meaningfulness of latent class proportions.”
Building Links Between Early Socioeconomic Status, Cognitive Ability, and Math and Science Achievement (2017, methodology): “To determine good model–data fit, recommended cutoffs are > .90 for the CFI and TLI and < .05 for the RMSEA (Kline, 2011; Mueller & Hancock, 2010).”
Understanding and Promoting Thinking About Knowledge (2016, background): “Researchers interested in developing such measures should endeavor to understand and meet epistemic ideals for psychometric validity (e.g., Bandalos & Finney, 2010; Knapp & Mueller, 2010) and should fully report the evidence regarding such efforts in their empirical work.”
Evidence Regarding the Internal Structure: Confirmatory Factor Analysis (2017, background): “Whichever fit indexes are used, most methodologists agree that more than one, and probably several, should be used to increase confidence in model fit (Bandalous & Finney, 2010).”
Disentangling the Roles of Institutional and Individual Poverty in the Identification of Gifted Students (2018, background): “…in more appropriate standard error estimates, and allows researchers to ask and answer nuanced questions about interactions that occur across the multiple levels of analysis (McCoach, 2010; McCoach & Adelson, 2010; Raudenbush & Bryk, 2002; Raudenbush, Bryk, Cheong, Congdon, & Du Toit, 2011).”
One Size Doesn’t Fit All: Using Factor Analysis to Gather Validity Evidence When Using Surveys in Your Research (2019, methodology): “However, caution should be taken before adding or removing any parameters (Bandalos and Finney, 2010).”