Study 2.1: Semantic priming

All lexical covariates considered in the semantic priming study were based on the target words. Figure 24 shows the zero-order correlations among the lexical covariates considered in the selection.

Code 

# Using the following variables...
semanticpriming[, c('z_target_word_frequency', 'z_target_length', 
                    'z_target_number_syllables',
                    'z_target_phonological_Levenshtein_distance', 
                    'z_target_orthographic_Levenshtein_distance')] %>%
  
  # renamed for the sake of clarity
  rename('Word frequency' = z_target_word_frequency,
         'Number of letters' = z_target_length,
         'Number of syllables' = z_target_number_syllables,
         'Phonological Levenshtein distance' = z_target_phonological_Levenshtein_distance,
         'Orthographic Levenshtein distance' = z_target_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 24: Zero-order correlations among lexical covariates pretested in the semantic priming study.

Table 6 shows the results of the selection model.

Code 

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

confint_lexical_covariates_selection_semanticpriming_lmerTest = 
  readRDS('semanticpriming/frequentist_analysis/lexical_covariates_selection/results/confint_lexical_covariates_selection_semanticpriming_lmerTest.rds')

# Rename effects in plain language...
# first, in the summary object
rownames(KR_summary_lexical_covariates_selection_semanticpriming_lmerTest$coefficients) =
  rownames(KR_summary_lexical_covariates_selection_semanticpriming_lmerTest$coefficients) %>%
  str_replace(pattern = 'z_target_word_frequency', 
              replacement = 'Word frequency') %>%
  str_replace(pattern = 'z_target_length', 
              replacement = 'Number of letters') %>%
  str_replace(pattern = 'z_target_number_syllables', 
              replacement = 'Number of syllables') %>%
  str_replace(pattern = 'z_target_phonological_Levenshtein_distance', 
              replacement = 'Phonological Levenshtein distance') %>%
  str_replace(pattern = 'z_target_orthographic_Levenshtein_distance',
              replacement = 'Orthographic Levenshtein distance')

# next, in the confidence intervals object
rownames(confint_lexical_covariates_selection_semanticpriming_lmerTest) =
  rownames(confint_lexical_covariates_selection_semanticpriming_lmerTest) %>%
  str_replace(pattern = 'z_target_word_frequency', 
              replacement = 'Word frequency') %>%
  str_replace(pattern = 'z_target_length', 
              replacement = 'Number of letters') %>%
  str_replace(pattern = 'z_target_number_syllables', 
              replacement = 'Number of syllables') %>%
  str_replace(pattern = 'z_target_phonological_Levenshtein_distance', 
              replacement = 'Phonological Levenshtein distance') %>%
  str_replace(pattern = 'z_target_orthographic_Levenshtein_distance',
              replacement = 'Orthographic Levenshtein distance')


# Create table (using custom function from the 'R_functions' folder)
frequentist_model_table(
  KR_summary_lexical_covariates_selection_semanticpriming_lmerTest, 
  confidence_intervals = confint_lexical_covariates_selection_semanticpriming_lmerTest,
  caption = 'Mixed-effects model for the selection of lexical covariates in the semantic priming 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 6: Mixed-effects model for the selection of lexical covariates in the semantic priming study.
β SE 95% CI t p
(Intercept) 0.01 0.00 [0.00, 0.02] 1.19 .236
Word frequency -0.14 0.01 [-0.15, -0.13] -24.19 <.001
Number of letters 0.00 0.01 [-0.02, 0.02] 0.12 .903
Number of syllables 0.04 0.01 [0.02, 0.06] 4.02 <.001
Orthographic Levenshtein distance 0.03 0.01 [0.00, 0.05] 2.19 .029
Phonological Levenshtein distance 0.02 0.01 [-0.01, 0.04] 1.28 .199

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 number of syllables.


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

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