Study 2.2: Semantic decision

Figure 25 shows the zero-order correlations among the lexical covariates considered in the selection.

Code 

# Using the following variables...
semanticdecision[, c('z_word_frequency', 'z_word_length', 'z_number_syllables',
                     'z_phonological_Levenshtein_distance', 
                     'z_orthographic_Levenshtein_distance')] %>%
  
  # renamed for the sake of clarity
  rename('Word frequency' = z_word_frequency,
         'Number of letters' = z_word_length,
         'Number of syllables' = z_number_syllables,
         'Phonological Levenshtein distance' = z_phonological_Levenshtein_distance,
         'Orthographic Levenshtein distance' = z_orthographic_Levenshtein_distance) %>%
  
  # make correlation matrix (custom function from 'R_functions' folder)
  correlation_matrix() + 
  theme(plot.margin = unit(c(0, -0.5, 0.05, -3.78), 'in'))

Figure 25: Zero-order correlations for the lexical covariates pretested in the semantic decision study.

Table 7 shows the results of the selection model.

Code 

# Read in model and confidence intervals
KR_summary_lexical_covariates_selection_semanticdecision_lmerTest = 
  readRDS('semanticdecision/frequentist_analysis/lexical_covariates_selection/results/KR_summary_lexical_covariates_selection_semanticdecision_lmerTest.rds')

confint_lexical_covariates_selection_semanticdecision_lmerTest = 
  readRDS('semanticdecision/frequentist_analysis/lexical_covariates_selection/results/confint_lexical_covariates_selection_semanticdecision_lmerTest.rds')

# Rename effects in plain language...
# first, in the summary object
rownames(KR_summary_lexical_covariates_selection_semanticdecision_lmerTest$coefficients) =
  rownames(KR_summary_lexical_covariates_selection_semanticdecision_lmerTest$coefficients) %>%
  str_replace(pattern = 'z_word_frequency', 
              replacement = 'Word frequency') %>%
  str_replace(pattern = 'z_word_length', 
              replacement = 'Number of letters') %>%
  str_replace(pattern = 'z_number_syllables', 
              replacement = 'Number of syllables') %>%
  str_replace(pattern = 'z_orthographic_Levenshtein_distance',
              replacement = 'Orthographic Levenshtein distance') %>%
  str_replace(pattern = 'z_phonological_Levenshtein_distance', 
              replacement = 'Phonological Levenshtein distance')

# next, in the confidence intervals object
rownames(confint_lexical_covariates_selection_semanticdecision_lmerTest) =
  rownames(confint_lexical_covariates_selection_semanticdecision_lmerTest) %>%
  str_replace(pattern = 'z_word_frequency', 
              replacement = 'Word frequency') %>%
  str_replace(pattern = 'z_word_length', 
              replacement = 'Number of letters') %>%
  str_replace(pattern = 'z_number_syllables', 
              replacement = 'Number of syllables') %>%
  str_replace(pattern = 'z_orthographic_Levenshtein_distance',
              replacement = 'Orthographic Levenshtein distance') %>%
  str_replace(pattern = 'z_phonological_Levenshtein_distance', 
              replacement = 'Phonological Levenshtein distance')


# Create table (using custom function from the 'R_functions' folder)
frequentist_model_table(
  KR_summary_lexical_covariates_selection_semanticdecision_lmerTest, 
  confidence_intervals = confint_lexical_covariates_selection_semanticdecision_lmerTest,
  caption = 'Mixed-effects model for the selection of lexical covariates in the semantic decision study.') %>% 
  
  # Format
  kable_classic(full_width = FALSE, html_font = 'Cambria') %>%
  
  # Footnote describing abbreviations, random slopes, etc. 
  # LaTeX code used to format the text.
  footnote(escape = FALSE, threeparttable = TRUE, 
           # The <p> below is used to enter a margin above the footnote 
           general_title = '<p style="margin-top: 10px;"></p>', 
           general = paste('*Note*. &beta; = Estimate based on $z$-scored predictors; *SE* = standard error;',
                           'CI = confidence interval. By-participant random slopes were included for every effect.'))
Table 7: Mixed-effects model for the selection of lexical covariates in the semantic decision study.
β SE 95% CI t p
(Intercept) 0.05 0.00 [0.05, 0.06] 12.35 <.001
Word frequency -0.13 0.01 [-0.14, -0.11] -20.01 <.001
Number of letters 0.05 0.01 [0.03, 0.07] 5.20 <.001
Number of syllables 0.08 0.01 [0.07, 0.10] 10.80 <.001
Orthographic Levenshtein distance -0.13 0.01 [-0.15, -0.10] -10.23 <.001
Phonological Levenshtein distance 0.01 0.01 [-0.01, 0.03] 0.91 .361

Note. β = Estimate based on \(z\)-scored predictors; SE = standard error; CI = confidence interval. By-participant random slopes were included for every effect.

Considering the maximum correlation allowed (\(r\) = \(\pm\).70) and the results of the model, the variables that will be included as covariates in the main analysis are word frequency and orthographic Levenshtein distance.


Pablo Bernabeu, 2022. Licence: CC BY 4.0.
Thesis: https://doi.org/10.17635/lancaster/thesis/1795.

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