Growth Curve Analysis and Visualization Using R

By J. Wiley. 2014

538 citations recorded by the source corpus.

The book Growth Curve Analysis and Visualization Using R provides an up-to-date, practical introduction to visualizing and modeling time course and multilevel data. It is particularly well-suited to applied researchers in the fields of cognitive science, neuroscience, and linguistics. Virtually no familiarity with R is required (although it helps). The book does assume a solid understanding of multiple regression, but does not assume prior knowledge or experience working with time course data. Detailed code examples are given using lme4 for linear and logistic growth curve models and ggplot2 for graphing.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4e202cfadb316df69ecaeee2fbc47326cd9b4eef

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In Artificial Intelligence

In Experimental and Cognitive Psychology

In Management Science and Operations Research

Citations in the corpus

Analyzing dynamic phonetic data using generalized additive mixed modeling: A tutorial focusing on articulatory differences between L1 and L2 speakers of English (2018, unknown): “For example, one can use growth curve analysis (Mirman, 2014; Mirman, Dixon, & Magnuson, 2008; see Winter & Wieling, 2016 for a tutorial introduction) which requires the researcher to provide the speci ﬁ cation of the non-linear pattern a priori .”
Incidental vocabulary learning in a natural reading context: an eye-tracking study (2017, methodology): “In so doing, we conducted a longitudinal analysis, or growth curve analysis (Cunnings, 2012; Mirman, 2014), of the repeated observations for a target word.”
The Comprehension of Counterfactual Conditionals: Evidence From Eye-Tracking in the Visual World Paradigm (2019, methodology): “We carried out a growth-curve analysis (Mirman, 2014; see the Supplementary Material B for details) which showed that people looked at the affirmative image more for the indicative conditional than the counterfactual, and they looked at the negative image more for the counterfactual than the…”
Cross-linguistic differences in case marking shape neural power dynamics and gaze behavior during sentence planning. (2022, unknown): “Speech onset latencies were modeled Eye tracking and EEG data were modeled following a growth curve approach (Mirman, 2014; Mirman, Dixon, & Magnuson, 2008), which employs orthogonalized polynomial time terms to describe non-linear changes over time.”