Analysis of Multivariate Social Science Data
By T. Liao. 2010
251 citations recorded by the source corpus.
Subfields: General Social Sciences, Information Systems
View Paper: https://www.semanticscholar.org/paper/10a44b154d1d0db5bfcb3a8148728c26741f7ec0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In General Social Sciences
- Kathryn Bartimote-Aufflick
- L. Smith
- Xiuping Jia
- Adam J. Bridgeman
- Shauna J. Sweet
- Chunyang Li
- Andre A. Rupp
- Ricardo M. A. Silva
In Information Systems
- Kathryn Bartimote-Aufflick
- L. Smith
- Xiuping Jia
- Adam J. Bridgeman
- Shauna J. Sweet
- Chunyang Li
- Andre A. Rupp
- Ricardo M. A. Silva
Citations in the corpus
- Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications (2017, background): “This family of models contains special cases, such as the linear factor analysis model (e.g. Anderson, 2003; Bartholomew et al., 2008), multidimensional item response theory model for binary responses that plays a key role in educational assessment (e.”
- Gaussian Process Structural Equation Models with Latent Variables (2010, methodology): “As discussed in Section 2.1, the corresponding measurement model is identifiable by fixing the structural equation for one indicator of each variable to have a zero intercept and unit slope (Bartholomew et al., 2008).”
- Analysis of Multivariate Social Science Data (2nd ed.) (2011, background): “We reviewed the second edition of this book; for an alternative review of this edition see Mair (2009), and for a review of the first edition see Austin (2003).”
- An Analytic Framework for Evaluating the Validity of Concept Inventory Claims (2015, background): “A detailed discussion of factor analysis is beyond the scope of this Appendix; instead, see Bartholomew, Steele, and Moustaki (2008) and Tabachnick and Fidell (2013).”
- Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks (2016, methodology): “potential factors from observed variables to replace original data [46].”
- The study, evaluation, and improvement of university student self-efficacy (2016, methodology): “with ordinal data are given in recent and comprehensive texts (e.g. de Vaus 2002; Agresti 2007, 2010, 2012; Bartholomew et al. 2008; Stevens 2009; Bartholomew, Knott, and Moustaki 2011).”