A multivariate approach to lexical decision latencies

  • Baayen et al. (2006) used data provided by Balota et al. (2004) to investigate the contributions of several additional predictor variables to subjects’ mean reaction times in lexical decision experiments. Baayen (2013) provided the data
  • Here we will consider the following of their predictors:
    • Familiarity: subjective familiarity ratings
    • AgeSubject: young or older speakers
    • WrittenFrequency: logged frequency of the word in the CELEX database
    • WordCategory: Part-of-speech category of the word
    • LengthInLetters: word length in letters
    • FrequencyInitialDiphoneWord: logged frequency of the combination of the first two letters

1. Loading and exploring the data

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2. Getting to know the data: the dependent variable RTlexdec

2.1 Central tendency

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2.2 Dispersion

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3. Getting to know the data: Familiarity

3.1 Central tendency

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3.2 Dispersion

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4. Getting to know the data: AgeSubject

4.1 Counts

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5. Getting to know the data: WrittenFrequency

5.1 Central tendency

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5.2 Dispersion

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6. Getting to know the data: WordCategory

6.1 Counts

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7. Getting to know the data: LengthInLetters

7.1 Central tendency

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7.2 Dispersion

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8. Getting to know the data: FrequencyInitialDiphoneWord

8.1 Central tendency

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8.2 Dispersion

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9. Removing outliers

  • Our previous exercises have shown that there are outliers in many of the variables. We should remove them before we continue
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10. Exploring the relationships between the dependent variable and the independent variables

  • We should perform pairwise plotting and correlation tests to see if the numeric independent variables are linearly related to the dependent variable and how strong their correlations are.

10.1 Familiarity vs RTlexdec

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10.2 WrittenFrequency vs RTlexdec

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10.3 LengthInLetters vs RTlexdec

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10.4 FrequencyInitialDiphoneWord vs RTlexdec

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11. Exploring the relationships between the independent variables

  • To make sure that our independent variables are not too correlated, we should explore their correlations too.
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12. Fitting an initial model to validate the assumptions

  • We know our data in and out
  • We have checked for outliers and excluded them
  • We have established correlations with the dependent variable
  • We have checked for correlations between variables
  • Now it is time to define a first model to check the model assumptions further
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13. Fitting an initial model to validate the assumptions

  • Recall that these are our assumptions:
    • There are no outliers/overly influential observations
    • The relationships are linear
    • There is no multicollinearity
    • The observations are independent from one another (there is no autocorrelation)
    • The residuals are not autocorrelated
    • The variability of the residuals does not increase or decrease with the explanatory variables or the response (i.e., there is no heteroskedasticity)
    • The residuals follow the normal distribution. This is less important if the sample size is large

13.1 There are no overly influential observations

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13.2 The relationships are linear

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13.3 There is no multicollinearity

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13.4 The residuals and the observations are not autocorrelated

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13.5 There is no heteroskedasticity (1/2)

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13.5 There is no heteroskedasticity (2/2)

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13.6 The residuals are normally distributed

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References

  • Balota, D., Cortese, M., Sergent-Marshall, S., Spieler, D. and Yap, M. (2004) Visual word recognition for single-syllable words, Journal of Experimental Psychology:General, 133, 283-316.
  • Baayen, R.H., Feldman, L. and Schreuder, R. (2006) Morphological influences on the recognition of monosyllabic monomorphemic words, Journal of Memory and Language, 53, 496-512.
  • Baayen, R. H. (2013). languageR: Data sets and functions with: Analyzing Linguistic Data: A practical introduction to statistics. https://CRAN.R-project.org/package=languageR .

© 2018 Jeroen Claes