Growth Curve Analysis


Growth curve analysis (GCA) is a multilevel regression technique designed for analysis of time course or longitudinal data. A major advantage of this approach is that it can be used to simultaneously analyze both group-level effects (e.g., experimental manipulations) and individual-level effects (i.e., individual differences). We have been using this method for several years, particularly in the context of visual world paradigm (VWP) eye tracking data and learning curves, though it can be applied to any time course data.

Mirman, D. (2014). Growth Curve Analysis and Visualization Using R. Chapman and Hall / CRC.

      • Examples.Rdata file contains example data sets that are discussed in the book and can be used to complete the exercises.
      • Solutions to the exercises are provided in ExerciseSolutions.pdf.

Mirman, D., Dixon, J.A., & Magnuson, J.S. (2008). Statistical and computational models of the visual world paradigm: Growth curves and individual differences. Journal of Memory and Language, 59(4), 475-494.

The basics are summarized below. Detailed tutorial materials are available on the LCDL github page. All of the analyses are conducted using the R package lme4 and the graphs generated using the ggplot2 package. For more information about R, check out our R Resources page.

Key challenges in analyzing time series data

  1. Using separate analyses for individual time bins or time windows creates a trade-off between power (more data in each bin) and temporal resolution (smaller time bins) and introduces experimenter bias in selection of time bins/windows.
  2. Statistical thresholding (i.e., p < 0.05 is significant but p > 0.05 is not) creates false discretization of continuous processes.
  3. There is no clear way to quantify individual differences, which are an important source of constraints for theories of cognition.

The big picture

Growth curve analysis provides a way to address those challenges by explicitly modeling change over time and quantifying both group-level and individual-level differences. To specify a growth curve model, you need to decide on three key components:

  1. The functional form: the overall shape of the data. What family of mathematical functions are you going to use to model the data? For growth curve analysis (and multilevel regression in general) the functional form needs to be "dynamically consistent", meaning that the model of the average is equal to the average of the individual models (see our technical report on dynamic consistency for more information). Polynomial models (starting from linear and going up through quadratic, cubic, quartic, etc.) satisfy this property and are able to capture any data shape, so they are a good option. Unfortunately, polynomials are not very good at capturing flat asymptotes and extrapolation (i.e., predicting what will happen outside the observed time window) is generally not possible. If you are not very interested in asymptotic portions of your data and don't plan on making out-of-time-window prediction, then a polynomial functional form is probably best for you.
  2. The fixed effects: What are your group-level predictors? Usually these are the experimental manipulations like word frequency, stimulus relatedness, etc. They can be continuous or categorical and they can refer to either the stimuli (e.g., word frequency) or the participants (e.g., age, short-term memory span, lesion location, etc.).
  3. The random effects: the randomly sampled observations over which you plan to generalize. Typically these are either participants or items. By capturing individual variability, the random effects provide another way to quantify individual differences.

A simple example

To see how these components come together, consider the ChickWeight data set (part of the default R installation), which has data from an experiment on the effect of diet on early growth of chicks. We start out with just a "base" model of chick growth allowing for individual variability in weight (in technical terms, a random intercept for each Chick: (1 | Chick) ):

> m.base <- lmer(weight ~ Time + (1 | Chick), data=ChickWeight, REML=F)

To this, we can add fixed effects of diet on the intercept (i.e., a constant difference in weights among chicks randomly assigned to different diets):

> m.0 <- lmer(weight ~ Time + Diet + (1 | Chick), data=ChickWeight, REML=F)

and on the slope (i.e., effects of diet on the rate of growth):

> m.1 <- lmer(weight ~ Time * Diet + (1 | Chick), data=ChickWeight, REML=F)

We can visually examine the effect of adding these terms by plotting the model fits against the observed data. The observed data are on the left, the m.0 (intercept model) fit is in the middle, and the m.1 (linear model) fit is on the right:

To statistically evaluate the effects of adding these terms, we can examine the change in the goodness of fit (log likelihood) through model comparisons:

> anova(m.base, m.0, m.1)
       Df    AIC    BIC  logLik deviance   Chisq Chi Df Pr(>Chisq)     
m.base  4 5630.3 5647.8 -2811.2   5622.3                               
m.0     7 5619.2 5649.7 -2802.6   5605.2  17.143      3  0.0006603 *** 
m.1    10 5508.0 5551.6 -2744.0   5488.0 117.184      3  < 2.2e-16 ***

The critical statistic is a chi-square with degrees of freedom equal to the number of parameters added. Since there were 4 diets, each effect of Diet added 3 parameters to the model (diet 1 is considered the reference condition by default and parameters are estimated for each of the 3 other diets relative to this baseline). The print command will give us the actual parameter estimates:

> coef(summary(m.1))

               Estimate Std. Error     t value 
(Intercept)  31.5080726  5.9112888  5.33015279 
Time          6.7130158  0.2573086 26.08935872 
Diet2        -2.8744770 10.1918475 -0.28203690 
Diet3       -13.2577473 10.1918475 -1.30081885 
Diet4        -0.3982789 10.2006418 -0.03904449 
Time:Diet2    1.8961205  0.4267194  4.44348327 
Time:Diet3    4.7098552  0.4267194 11.03735892 
Time:Diet4    2.9494975  0.4322552  6.82350985

Of course, in addition to different baseline weights, the chicks might have other (unmeasured) individual properties that affect their growth rate. To capture this, we can add a linear effect on time of Chick to the random effects, and we can use model comparisons to examine whether this effect improved model fit:

> m.t1 <- lmer(weight ~ Time * Diet + (1 + Time | Chick), data=ChickWeight, REML=F)
> anova(m.1, m.t1)
     Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)     
m.1  10 5508.0 5551.6 -2744.0   5488.0                              
m.t1 12 4824.2 4876.5 -2400.1   4800.2 687.78      2  < 2.2e-16 ***
> coef(summary(m.t1))
              Estimate Std. Error   t value 
(Intercept)  33.654115  2.8023251 12.009354 
Time          6.279857  0.7303555  8.598357 
Diet2        -5.020519  4.8072243 -1.044370 
Diet3       -15.403790  4.8072243 -3.204300 
Diet4        -1.747533  4.8145791 -0.362967 
Time:Diet2    2.329279  1.2507982  1.862234 
Time:Diet3    5.143014  1.2507982  4.111785 
Time:Diet4    3.252804  1.2515485  2.599023

Adding the random slopes did improve model fit. As a general rule, the model should include all random effects that are licensed by the design, that is, all the ones that could vary across participants (Barr et al., 2013). Notice that adding the random slope effect caused slight changes in the fixed effect parameter estimates because some of the variance was now captured by that random effect. This is why Barr et al. recommend using the "maximal" random effect structure -- even when a random effect does not improve model fit, it can still affect the fixed effect estimates and excluding it can elevate the false positive rate.

Applying GCA to VWP data

Orthogonal polynomials

Often, our data are not perfectly straight, so we want to capture that curvature with higher-order polynomial terms (time squared, time cubed, etc.). Because our time variable is usually only positive, natural polynomials are correlated, so the estimated parameters will be interdependent. Given a particular time range and a maximum polynomial order, we can transform the polynomial time vectors to make them independent, that is, "orthogonal". This is illustrated in the figure below for linear and quadratic time in the range 1-10. Since orthogonal polynomial time terms are independent, the parameter estimates can be interpreted independently.

With orthogonal polynomials, the intercept term reflects the average overall curve height, rather than the height at the left edge of the time window, so if you are interested in differences at the very beginning of the time window, you may be better off sticking with natural polynomials.

Reporting results

There are two rules of thumb for reporting growth curve analysis results:

  1. Clearly describe each of the three key components of the model: the functional form (third-order orthogonal polynomial), the fixed effects (effect of Condition on all time terms), and the random effects (effect of Subject on each of the time terms and nested effects of Subject-by-Condition on each of the time terms except the cubic). Depending on the circumstances and complexity of the model, you may want to include additional information about the factors and why they were included or not. It's also a good idea to report which method was used for computing p-values.
  2. For key findings, report parameter estimates and standard errors along with significance tests. In some cases the model comparison is going to be enough, but for key findings, the readers should want to see the parameter estimates. The parameter estimate standard errors are critical for interpreting the estimates, so those should be reported as well. The t-values are not critical to report (they are just Estimate divided by the Std Error, so they can always be computed from the reported estimates and standard errors). If there are many estimated parameters, it may be a good idea to focus the main text discussion on the most important ones and report the full set in a table or appendix.

Worked examples

Target fixation proportions for two within-participants conditions (high vs. low word frequency) modeled with third-order orthogonal polynomials.

Individual differences in cohort and rhyme competition among people with aphasia and age-matched controls (Mirman et al., 2011), modeled with fourth-order orthogonal polynomials.

Individual differences in the time course of activation of function and thematic semantic knowledge in 17 participants with left hemisphere stroke (Kalenine, Mirman, & Buxbaum, 2012).

Novel word learning in two between-participants conditions (high vs. low transitional probability; based on Mirman et al., 2008) modeled using second-order orthogonal polynomial.

Additional worked examples and explanations are included in the growth curve analysis workshop slides.

Visualizing growth curve models

I highly recommend the ggplot2 package for plotting (if you are not already familiar with it, check out our R Resources page). To start, plot the target fixation data discussed above:

ggplot(TargetFix, aes(Time, meanFix, color=Condition)) + 
  stat_summary(, geom="pointrange") + 
  labs(y="Fixation Proportion", x="Time since word onset (ms)") + 
  theme_bw() + scale_color_manual(values=c("red", "blue"))

You can now add the model fits by adding another stat_summary with the y-variable mapped to the predicted values in the lmer model object, which can be accessed using the fitted() function:

last_plot() + stat_summary(aes(y=fitted(m.full)), fun.y=mean, geom="line")

The other aesthetics (mappings) are automatically inherited (e.g., mapping of color to Condition), so the plot will automatically remain consistent. This is particularly useful for multi-panel plots as in the cohort and rhyme competition example from above. When the model was fit to a data subset, you may want to make sure to align the data and the model-predicted values by creating a new data frame that has just the data subset and add the model-predicted values to that data frame:

dat <- subset(CohortRhyme, Type == "Cohort")
dat$mfit <- fitted(
ggplot(dat, aes(Time, FixProp, color=Object)) + 
  facet_wrap(~ Group) + theme_bw() + 
  stat_summary(fun=mean, geom="point") +
  stat_summary(aes(y=mfit), fun.y=mean, geom="line") +
  labs(y="Fixation Proportion", x="Time since word onset (ms)") + 
  scale_color_manual(values=c("red", "blue"))
Cohort data and model fit