title

output

Beyond consistency: Contextual dependency of language style in monologue and conversation

html_document

keep_md	number_sections
true	true

This R markdown provides the data preparation and analyses for our manuscript, "Beyond consistency: Contextual dependency of language style in monologue and conversation" (Mueller-Frommeyer, Kauffeld, & Paxton, accepted, Cognitive Science).

To run this from scratch, you will need the following files:

The data is accessible in our OSF project (https://osf.io/3jx2g/). Please create a folder called data, with a subfolder LIWC-results, with a subfolder RQA with two sub-folders monologues and conversations in your working directory. Add the data from OSF to the monologue and conversation folders.
./scripts/bc-libraries_and_functions.r: Loads in necessary libraries and creates new functions for our analyses.

Code written by: L. C. Mueller-Frommeyer (Technische Universitaet Braunschweig) & A. Paxton (University of Connecticut)

Date last modified: 06 April 2020

Preliminaries

# clear everything
rm(list=ls())

# load libraries and add new functions
source('./scripts/bc-libraries_and_functions.r')

Data preparation

Recurrence quantification analysis

Monologues

# read in all monologue files
mon_files = list.files('./data/LIWC-results/RQA/Monologues',
                       pattern = ".txt", full.names = TRUE)
mon_dfs = plyr::ldply(mon_files,
                      read.table, sep="\t", dec = ",", header=TRUE) #added decimal to get numbers instead of characters

# prepare monologues for RQA
mon_dfs = mon_dfs %>%
  
  # separate 'Filename' column into separate columns
  tidyr::separate(Filename,
                  into = c("dyad_id", "dyad_position", "speaker_code"),
                  sep = '_',
                  remove = FALSE,
                  extra = "drop",
                  fill = "warn") %>%
  
  
  # extract speaker number ID and conversation type from variable
  mutate(cond = gsub("[[:digit:]]+","",dyad_id)) %>%
  
  # create new variable function_contrast with all 0 replaced by -1
  dplyr::rename(function_words = function.) %>%
  mutate(function_contrast = dplyr::if_else(function_words==0,
                                            -1,
                                            function_words)) %>%
  
  #add new variable specifying conversation type
  mutate(conv.type = "M")

# split dataframe by monologue
split_mon = split(mon_dfs, list(mon_dfs$Filename))

# cycle through the individual monologues
rqa_mon = data.frame()
for (next_mon in split_mon){
  
  # run (auto-)recurrence
  rqa_for_mon = crqa(ts1=next_mon$function_words,
                     ts2=next_mon$function_contrast,
                     delay=1,
                     embed=1,
                     r=0.1,
                     normalize=0,
                     rescale=0,
                     mindiagline=2,
                     minvertline=2,
                     tw=1, # exclude line of identity
                     whiteline=FALSE,
                     recpt=FALSE)
  
  # save plot-level information to dataframe
  dyad_id = unique(next_mon$dyad_id)
  speaker_code = unique(next_mon$speaker_code)
  cond = NA   #changed it to NA as there was no condition in the monologue
  conv.type = unique(next_mon$conv.type)
  next_data_line = data.frame(dyad_id,  
                              speaker_code,
                              conv.type,
                              cond,
                              rqa_for_mon[1:9]) %>%
    mutate(rNRLINE = NRLINE / dim(next_mon)[1]) # normalize NRLINE by number of words
  rqa_mon = rbind.data.frame(rqa_mon,next_data_line)
  
  # save the RPs -- including LOI/LOS for plotting
  # rqa_for_mon = crqa(ts1=next_mon$function_words,
  #                    ts2=next_mon$function_contrast,
  #                    delay=1,
  #                    embed=1,
  #                    r=0.1,
  #                    normalize=0,
  #                    rescale=0,
  #                    mindiagline=2,
  #                    minvertline=2,
  #                    tw=0, # include LOI/LOS
  #                    whiteline=FALSE,
  #                    recpt=FALSE)
  # png(filename = paste0('./figures/monologue/rp-speaker_',speaker_code,'-monologue.png'))
  # plotRP(rqa_for_mon$RP,
  #        list(unit = 2, labelx = "Speaker A", labely = "Speaker A",
  #             cols = "black", pcex = .5))
  # dev.off()
}

# clean up what we don't need
rm(split_mon, next_mon, rqa_for_mon,
   dyad_id, speaker_code, cond, conv.type, next_data_line)

Conversations

# read in all conversation files
conv_files = list.files('./data/LIWC-results/RQA/Conversations-1',
                        pattern = ".txt", full.names = TRUE)
conv_dfs = plyr::ldply(conv_files,
                       read.table, sep="\t", dec = ",", header=TRUE)

# prepare conversations for RQA
conv_dfs = conv_dfs %>%
  
  # separate 'Filename' column into separate columns
  tidyr::separate(Filename,
                  into = c("dyad_id", "dyad_position", "speaker_code"),
                  sep = '_',
                  remove = FALSE,
                  extra = "drop",
                  fill = "warn") %>%
  
  # extract speaker number ID and conversation type from variable
  mutate(cond = gsub("[[:digit:]]+","",dyad_id)) %>%
  
  # create new variable function_contrast with all 0 replaced by -1
  dplyr::rename(function_words = function.) %>%
  mutate(function_contrast = dplyr::if_else(function_words==0,
                                            -1,
                                            function_words)) %>%
  
  # add new variable specifying conversation type
  mutate(conv.type = "C")

# split dataframe by conversation
split_conv = split(conv_dfs, list(conv_dfs$Filename))

# cycle through the individual conversations
rqa_conv = data.frame()
for (next_conv in split_conv){
  
  # run recurrence
  rqa_for_conv = crqa(ts1=next_conv$function_words,
                      ts2=next_conv$function_contrast,
                      delay=1,
                      embed=1,
                      r=0.1,
                      normalize=0,
                      rescale=0,
                      mindiagline=2,
                      minvertline=2,
                      tw=1, # exclude line of identity
                      whiteline=FALSE,
                      recpt=FALSE)
  
  # save plot-level information to dataframe
  dyad_id = unique(next_conv$dyad_id)
  speaker_code = unique(next_conv$speaker_code)
  conv.type = unique(next_conv$conv.type)
  cond = unique(next_conv$cond)
  next_data_line = data.frame(dyad_id,  
                              speaker_code,
                              conv.type,
                              cond,
                              rqa_for_conv[1:9]) %>%
    mutate(rNRLINE = NRLINE / dim(next_conv)[1]) # normalize NRLINE by number of words
  rqa_conv = rbind.data.frame(rqa_conv,next_data_line)
  
  # # plot the RPs -- include LOI/LOS
  # rqa_for_conv = crqa(ts1=next_conv$function_words,
  #                     ts2=next_conv$function_contrast,
  #                     delay=1,
  #                     embed=1,
  #                     r=0.1,
  #                     normalize=0,
  #                     rescale=0,
  #                     mindiagline=2,
  #                     minvertline=2,
  #                     tw=0, # retain LOI for plotting only
  #                     whiteline=FALSE,
  #                     recpt=FALSE)
  # png(filename = paste0('./figures/conversation/rp-speaker_',speaker_code,'-conversation.png'))
  # plotRP(rqa_for_conv$RP,
  #                  list(unit = 2, labelx = "Speaker A", labely = "Speaker A",
  #                       cols = "black", pcex = .01))
  # dev.off()
}

# clean up what we don't need
rm(split_conv, next_conv, rqa_for_conv,
   dyad_id, speaker_code, conv.type, cond, next_data_line)

Proportions

Monologues

# split monologues into separate dataframes
split_mon = split(mon_dfs, list(mon_dfs$Filename))

# create an empty frame for results
FW_freq_mon = data.frame()

# cycle through all monologues
for (next_mon in split_mon) {
  
  # count frequency of function words
  freq = sum(next_mon$function_words == 100)
  wc = sum(next_mon$WC == 1)
  speaker_code = unique(next_mon$speaker_code)
  
  # add results to dataframe
  data_temp = data.frame(freq, wc, speaker_code)
  
  
  FW_freq_mon = rbind.data.frame(FW_freq_mon, data_temp)
}
FW_freq_mon$prop = FW_freq_mon$freq/FW_freq_mon$wc

Conversations

# split conversations into separate dataframes
split_conv = split(conv_dfs, list(conv_dfs$Filename))

# create an empty frame for results
FW_freq_conv = data.frame()

# cycle through all conversations
for (next_conv in split_conv) {
  
  # count frequency of function words
  freq = sum(next_conv$function_words == 100)
  wc = sum(next_conv$WC == 1)
  speaker_code = unique(next_conv$speaker_code)
  
  # add results to dataframe
  data_temp = data.frame(freq, wc, speaker_code)
  FW_freq_conv = rbind.data.frame(FW_freq_conv, data_temp)
}

# convert from frequency to proportion
FW_freq_conv$prop = FW_freq_conv$freq/FW_freq_conv$wc

Create dataframes

We'll need to create two dataframes: an unstandardized dataframe (analysis_df) and a standardized one (standardized_df).

# bring together monologue rqa and freq data
data_mon = dplyr::left_join(rqa_mon, FW_freq_mon, by = "speaker_code")

# bring togehter conversation rqa and freq data
data_conv = dplyr::left_join(rqa_conv, FW_freq_conv, by = "speaker_code")

# bring together the monologue and conversation data
analysis_df = rbind(data_mon, data_conv) %>%
  
  # update coding for conversation type and condition
  mutate(conv.type = as.factor(dplyr::if_else(conv.type == "M",
                                              -.5,
                                              .5)),
         cond = as.factor(dplyr::if_else(cond == "P",
                                         -.5,
                                         .5)))

# save dataframe to file
write.table(analysis_df, './data/analysis_df.csv',
            sep=",", row.names=FALSE, col.names=TRUE)

# standardize the analysis dataframe
standardized_df = analysis_df %>%
  
  # convert things as needed to numeric
  mutate(dyad_id = as.numeric(as.factor(dyad_id)),
         speaker_code = as.numeric(as.factor(speaker_code))) %>%
  
  # standardize
  mutate_all(funs(as.numeric(scale(as.numeric(.))))) %>%
  
  # convert to factors as needed
  mutate(dyad_id = as.factor(dyad_id),
         speaker_code = as.factor(speaker_code),
         conv.type = as.factor(conv.type),
         cond = as.factor(cond))

## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas: 
## 
##   # Simple named list: 
##   list(mean = mean, median = median)
## 
##   # Auto named with `tibble::lst()`: 
##   tibble::lst(mean, median)
## 
##   # Using lambdas
##   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.

# save dataframe to file
write.table(standardized_df, './data/standardized_df.csv',
            sep=",", row.names=FALSE, col.names=TRUE)

Data analysis

Power Sensitivity Analyses

Power analyses for d = .4

First, we run power sensitivity analysis for our model using recommendations from Brysbaert and Stevens (2018). We run the power analysis for the standardized model using effect sizes between d = .4 and d = .1 to detect the smallest possible effect size that can be found with a power of at least 80%.

# power analyses for the standardized model
planned_analyses_st <- lmer(RR ~ conv.type + (1|speaker_code),
                            data = standardized_df, REML = FALSE)

# change the fixed effect to the assumed effect size of .4 (Brysbaert & Stevens, 2018)
#   (although with a negative sign)
fixef(planned_analyses_st)["conv.type0.997879106838302"] = -0.4

# run power analysis for our current sample, with a default significance
#   criterion of .05 and an effect size of .4
power.4 <- powerSim(planned_analyses_st,
                  nsim = 1000)

## Power for predictor 'conv.type', (95% confidence interval):
##       96.00% (94.59, 97.13)
## 
## Test: Likelihood ratio
## 
## Based on 1000 simulations, (13 warnings, 0 errors)
## alpha = 0.05, nrow = 236
## 
## Time elapsed: 0 h 1 m 5 s

Power analyses for d = .3

First, we run a power analysis for our model using recommendations from Brysbaert and Stevens (2018). We run the power analysis for the standardized model.

# power analyses for the standardized model
planned_analyses_st <- lmer(RR ~ conv.type + (1|speaker_code),
                            data = standardized_df, REML = FALSE)

# change the fixed effect to the assumed effect size of .4 (Brysbaert & Stevens, 2018)
#   (although with a negative sign)
fixef(planned_analyses_st)["conv.type0.997879106838302"] = -0.3

# run power analysis for our current sample, with a default significance
#   criterion of .05 and an effect size of .4
power.3 <- powerSim(planned_analyses_st,
                  nsim = 1000)

## Power for predictor 'conv.type', (95% confidence interval):
##       81.50% (78.95, 83.86)
## 
## Test: Likelihood ratio
## 
## Based on 1000 simulations, (13 warnings, 0 errors)
## alpha = 0.05, nrow = 236
## 
## Time elapsed: 0 h 1 m 6 s

Additional Power analyses d = .29 to see if the power is below 80%

First, we run a power analysis for our model using recommendations from Brysbaert and Stevens (2018). We run the power analysis for the standardized model.

# power analyses for the standardized model
planned_analyses_st <- lmer(RR ~ conv.type + (1|speaker_code),
                            data = standardized_df, REML = FALSE)

# change the fixed effect to the assumed effect size of .4 (Brysbaert & Stevens, 2018)
#   (although with a negative sign)
fixef(planned_analyses_st)["conv.type0.997879106838302"] = -0.29

# run power analysis for our current sample, with a default significance
#   criterion of .05 and an effect size of .4
power.29 <- powerSim(planned_analyses_st,
                  nsim = 1000)

## Power for predictor 'conv.type', (95% confidence interval):
##       79.90% (77.28, 82.34)
## 
## Test: Likelihood ratio
## 
## Based on 1000 simulations, (7 warnings, 0 errors)
## alpha = 0.05, nrow = 236
## 
## Time elapsed: 0 h 1 m 2 s

Power analyses for d = .2

First, we run a power analysis for our model using recommendations from Brysbaert and Stevens (2018). We run the power analysis for the standardized model.

# power analyses for the standardized model
planned_analyses_st <- lmer(RR ~ conv.type + (1|speaker_code),
                            data = standardized_df, REML = FALSE)

# change the fixed effect to the assumed effect size of .4 (Brysbaert & Stevens, 2018)
#   (although with a negative sign)
fixef(planned_analyses_st)["conv.type0.997879106838302"] = -0.2

# run power analysis for our current sample, with a default significance
#   criterion of .05 and an effect size of .4
power.2 <- powerSim(planned_analyses_st,
                  nsim = 1000)

## Power for predictor 'conv.type', (95% confidence interval):
##       44.70% (41.59, 47.84)
## 
## Test: Likelihood ratio
## 
## Based on 1000 simulations, (13 warnings, 0 errors)
## alpha = 0.05, nrow = 236
## 
## Time elapsed: 0 h 1 m 2 s

Power analyses for d = .1

First, we run a power analysis for our model using recommendations from Brysbaert and Stevens (2018). We run the power analysis for the standardized model.

# power analyses for the standardized model
planned_analyses_st <- lmer(RR ~ conv.type + (1|speaker_code),
                            data = standardized_df, REML = FALSE)

# change the fixed effect to the assumed effect size of .4 (Brysbaert & Stevens, 2018)
#   (although with a negative sign)
fixef(planned_analyses_st)["conv.type0.997879106838302"] = -0.1

# run power analysis for our current sample, with a default significance
#   criterion of .05 and an effect size of .4
power.1 <- powerSim(planned_analyses_st,
                  nsim = 1000)

## Power for predictor 'conv.type', (95% confidence interval):
##       14.30% (12.19, 16.62)
## 
## Test: Likelihood ratio
## 
## Based on 1000 simulations, (6 warnings, 0 errors)
## alpha = 0.05, nrow = 236
## 
## Time elapsed: 0 h 1 m 5 s

Descriptives and correlation tables

Here we generate descriptive statistics and intercorrelations for our variables, separated by monologue and conversation. Data are saved to files and reported as Supplementary Materials for the paper.

# Prepare data for descriptive analysis - only leave numerical elements in data frame
Descriptives_mon =  data_mon %>%
  select(-dyad_id, -speaker_code, -conv.type, -cond, -freq, -wc, -NRLINE, -ENTR)
Descriptives_conv =  data_conv %>%
  select(-dyad_id, -speaker_code, -conv.type, -cond, -freq, -wc, -NRLINE, -ENTR)

# Mean, Min, Max for monologues and dialogues
mean_mon = psych::describe(Descriptives_mon)
mean_conv = psych::describe(Descriptives_conv)

# write descriptives to files
write.table(mean_mon, './data/mean_mon.csv',
            sep=",", row.names=FALSE, col.names=TRUE)
write.table(mean_conv, './data/mean_conv.csv',
            sep=",", row.names=FALSE, col.names=TRUE)

# Intercorrelations for monologues
p_cor_mon = rcorr(as.matrix(Descriptives_mon))
p_mon = p_cor_mon$P
r_mon = p_cor_mon$r

# Intercorrelations for dialogues
p_cor_conv = rcorr(as.matrix(Descriptives_conv))
p_conv = p_cor_conv$P
r_conv = p_cor_conv$r

# save intercorelations to file
write.table(p_mon, './data/p_mon.csv',
            sep=",", row.names=FALSE, col.names=TRUE)
write.table(r_mon, './data/r_mon.csv',
            sep=",", row.names=FALSE, col.names=TRUE)
write.table(p_conv, './data/p_conv.csv',
            sep=",", row.names=FALSE, col.names=TRUE)
write.table(r_conv, './data/r_conv.csv',
            sep=",", row.names=FALSE, col.names=TRUE)

Planned analysis

Here, we perform a linear mixed-effects model to analyze how conversation setting---whether a monologue (M) or conversation (C)---changes a person's language style, specifically looking at their use of function words (often a measure of syntactic complexity and structure).

We attempted to analyze the data using maximal random effects structures and an uncorrelated random intercept within the random slope, but both models failed to converge. As a result, we use only the random intercept in our models. We include both raw and standardized models below.

RR: Raw model

# raw: does linguistic style change based on the conversation setting?
planned_analyses_raw <- lmer(RR ~ conv.type + (1|speaker_code),
                             data = analysis_df, REML = FALSE)

# calculate 95% CI for model
CI_planned_analyses_raw <- confint(planned_analyses_raw)


# calculate effect size for model
es_planned_analyses_raw <- lme.dscore(planned_analyses_raw,
                                      data = analysis_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: RR ~ conv.type + (1 | speaker_code)
##    Data: analysis_df
## 
##      AIC      BIC   logLik deviance df.resid 
##   1470.0   1483.8   -731.0   1462.0      232 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6419 -0.5827 -0.0083  0.6212  2.6381 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept)  5.627   2.372   
##  Residual                 23.621   4.860   
## Number of obs: 236, groups:  speaker_code, 118
## 
## Fixed effects:
##              Estimate Std. Error       df t value             Pr(>|t|)    
## (Intercept)   29.3813     0.4979 227.5761  59.015 < 0.0000000000000002 ***
## conv.type0.5   4.7168     0.6327 117.9998   7.455      0.0000000000165 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## conv.typ0.5 -0.635

##                     t       df        d
## conv.type0.5 7.422877 116.9998 1.372492

##                   2.5 %    97.5 %
## .sig01        0.7130132  3.399938
## .sigma        4.3003473  5.552880
## (Intercept)  28.4014071 30.361278
## conv.type0.5  3.4664829  5.967112

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	29.38	0.4979	227.6	59.01	0.0001	0.0001	***
conv.type0.5	4.717	0.6327	118	7.455	0.0001	0.0001	***

RR: Standardized model

# standardized: does linguistic style change based on the conversation setting?
planned_analyses_st <- lmer(RR ~ conv.type + (1|speaker_code),
                            data = standardized_df, REML = FALSE)

# calculate 95%CI for model
CI_planned_analyses_st <- confint(planned_analyses_st)

# calculate effect size for model
es_planned_analyses_st <- lme.dscore(planned_analyses_st,
                                     data = standardized_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: RR ~ conv.type + (1 | speaker_code)
##    Data: standardized_df
## 
##      AIC      BIC   logLik deviance df.resid 
##    631.2    645.1   -311.6    623.2      232 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6419 -0.5827 -0.0083  0.6212  2.6381 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept) 0.1610   0.4012  
##  Residual                 0.6757   0.8220  
## Number of obs: 236, groups:  speaker_code, 118
## 
## Fixed effects:
##                            Estimate Std. Error       df t value
## (Intercept)                 -0.3989     0.0842 227.5761  -4.737
## conv.type0.997879106838302   0.7977     0.1070 117.9998   7.455
##                                   Pr(>|t|)    
## (Intercept)                0.0000038179014 ***
## conv.type0.997879106838302 0.0000000000165 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## c.0.9978791 -0.635

##                                   t       df        d
## conv.type0.997879106838302 7.422877 116.9998 1.372492

##                                 2.5 %     97.5 %
## .sig01                      0.1205920  0.5750321
## .sigma                      0.7273184  0.9391594
## (Intercept)                -0.5646130 -0.2331397
## conv.type0.997879106838302  0.5862868  1.0092185

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	-0.3989	0.0842	227.6	-4.737	0.0001	0.0001	***
conv.type0.997879106838302	0.7978	0.107	118	7.455	0.0001	0.0001	***

As predicted, we do see a difference in linguistic style between conversations and monologues. Specifically, we find that conversations tend to have more recurrence in their use of function words than monologues do.

Validating RR against proportion

To validate the use of RQA (dynamic) metrics against standard (static) metrics, we next build a similar model predicting proportion with conversation type. If RR and proportion tap into similar underlying dynamics, we would expect to see congruent results between the two methods.

Also, we calculated a model for a more traditional static measure of language style- namely, proportion of function words to compare the new dynamic approach to the traditional more static approach.

Raw model

# raw: does linguistic style change based on the conversation setting?
exploratory_analyses_raw_prop <- lmer(prop ~ conv.type + (1|speaker_code),
                                      data = analysis_df, REML = FALSE)

# calculate 95% CI for model
CI_exploratory_analyses_raw_prop <- confint(exploratory_analyses_raw_prop)


# calculate effect size for model
es_exploratory_analyses_raw_prop <- lme.dscore(exploratory_analyses_raw_prop,
                                               data = analysis_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: prop ~ conv.type + (1 | speaker_code)
##    Data: analysis_df
## 
##      AIC      BIC   logLik deviance df.resid 
##   -755.1   -741.3    381.6   -763.1      232 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8212 -0.5559  0.0246  0.6374  2.4913 
## 
## Random effects:
##  Groups       Name        Variance  Std.Dev.
##  speaker_code (Intercept) 0.0004302 0.02074 
##  Residual                 0.0019175 0.04379 
## Number of obs: 236, groups:  speaker_code, 118
## 
## Fixed effects:
##                Estimate Std. Error         df t value             Pr(>|t|)
## (Intercept)    0.544571   0.004460 228.333848 122.089 < 0.0000000000000002
## conv.type0.5   0.038265   0.005701 117.999786   6.712       0.000000000707
##                 
## (Intercept)  ***
## conv.type0.5 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## conv.typ0.5 -0.639

##                     t       df        d
## conv.type0.5 6.683584 116.9998 1.235796

##                    2.5 %     97.5 %
## .sig01       0.002307412 0.03007312
## .sigma       0.038745409 0.05003022
## (Intercept)  0.535791810 0.55335036
## conv.type0.5 0.026999823 0.04953007

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	0.5446	0.00446	228.3	122.1	0.0001	0.0001	***
conv.type0.5	0.03826	0.005701	118	6.712	0.0001	0.0001	***

Standardized model

# standardized: does linguistic style change based on the conversation setting?
exploratory_analyses_st_prop <- lmer(prop ~ conv.type + (1|speaker_code),
                                     data = standardized_df, REML = FALSE)

# calculate 95%CI for model
CI_exploratory_analyses_st_prop <- confint(exploratory_analyses_st_prop)

# calculate effect size for model
es_exploratory_analyses_st_prop <- lme.dscore(exploratory_analyses_st_prop,
                                              data = standardized_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: prop ~ conv.type + (1 | speaker_code)
##    Data: standardized_df
## 
##      AIC      BIC   logLik deviance df.resid 
##    638.5    652.4   -315.3    630.5      232 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8212 -0.5559  0.0246  0.6374  2.4913 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept) 0.1578   0.3973  
##  Residual                 0.7036   0.8388  
## Number of obs: 236, groups:  speaker_code, 118
## 
## Fixed effects:
##                             Estimate Std. Error        df t value
## (Intercept)                 -0.36649    0.08544 228.33385  -4.289
## conv.type0.997879106838302   0.73298    0.10920 117.99979   6.712
##                                  Pr(>|t|)    
## (Intercept)                0.000026462838 ***
## conv.type0.997879106838302 0.000000000707 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## c.0.9978791 -0.639

##                                   t       df        d
## conv.type0.997879106838302 6.683584 116.9998 1.235796

##                                 2.5 %     97.5 %
## .sig01                      0.0441996  0.5760654
## .sigma                      0.7421874  0.9583535
## (Intercept)                -0.5346632 -0.1983206
## conv.type0.997879106838302  0.5171949  0.9487729

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	-0.3665	0.08544	228.3	-4.289	0.0001	0.0001	***
conv.type0.997879106838302	0.733	0.1092	118	6.712	0.0001	0.0001	***

We see a difference in proportion of function words between conversations and monologues. Specifically, we find that conversations tend to have a higher proportion of function words than monologues do.

The congruence between proportion-based models and RR-based models support the validity of the latter. However, while proportion can only give a single value of similarity, we can tap into other RQA metrics to understand the structure of those patterns.

Additional exploratory analyses

Next, we repeat our analyses for RQA parameters indicative of LS structure and complexity to explore the influence of conversational setting on aspects of language style structure and complexity in addition to recurrence. An explanation of the parameters under investigation can be found in the manuscript.

DET: Exploratory analyses

Raw model

# raw: does linguistic style change based on the conversation setting?
exploratory_analyses_raw_DET <- lmer(DET ~ conv.type + (1|speaker_code),
                                     data = analysis_df, REML = FALSE)

# calculate 95% CI for model
CI_exploratory_analyses_raw_DET <- confint(exploratory_analyses_raw_DET)


# calculate effect size for model
es_exploratory_analyses_raw_DET <- lme.dscore(exploratory_analyses_raw_DET,
                                              data = analysis_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: DET ~ conv.type + (1 | speaker_code)
##    Data: analysis_df
## 
##      AIC      BIC   logLik deviance df.resid 
##   1724.8   1738.6   -858.4   1716.8      231 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.2264 -0.5226  0.0869  0.5316  2.6415 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept) 22.17    4.708   
##  Residual                 67.74    8.230   
## Number of obs: 235, groups:  speaker_code, 118
## 
## Fixed effects:
##              Estimate Std. Error       df t value             Pr(>|t|)    
## (Intercept)   47.4584     0.8764 222.0860  54.153 < 0.0000000000000002 ***
## conv.type0.5   9.3213     1.0743 118.0736   8.676   0.0000000000000262 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## conv.typ0.5 -0.616

##                     t       df        d
## conv.type0.5 8.639275 117.0697 1.596927

##                  2.5 %    97.5 %
## .sig01        2.497910  6.392605
## .sigma        7.280670  9.405337
## (Intercept)  45.733293 49.183545
## conv.type0.5  7.198505 11.444304

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	47.46	0.8764	222.1	54.15	0.0001	0.0001	***
conv.type0.5	9.321	1.074	118.1	8.676	0.0001	0.0001	***

Standardized model

# standardized: does linguistic style change based on the conversation setting?
exploratory_analyses_st_DET <- lmer(DET ~ conv.type + (1|speaker_code),
                                    data = standardized_df, REML = FALSE)

# calculate 95%CI for model
CI_exploratory_analyses_st_DET <- confint(exploratory_analyses_st_DET)

# calculate effect size for model
es_exploratory_analyses_st_DET <- lme.dscore(exploratory_analyses_st_DET,
                                             data = standardized_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: DET ~ conv.type + (1 | speaker_code)
##    Data: standardized_df
## 
##      AIC      BIC   logLik deviance df.resid 
##    615.6    629.5   -303.8    607.6      231 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.2264 -0.5226  0.0869  0.5316  2.6415 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept) 0.1977   0.4446  
##  Residual                 0.6040   0.7772  
## Number of obs: 235, groups:  speaker_code, 118
## 
## Fixed effects:
##                             Estimate Std. Error        df t value
## (Intercept)                 -0.44188    0.08276 222.08597  -5.339
## conv.type0.997879106838302   0.88023    0.10145 118.07363   8.676
##                                      Pr(>|t|)    
## (Intercept)                0.0000002301176448 ***
## conv.type0.997879106838302 0.0000000000000262 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## c.0.9978791 -0.616

##                                   t       df        d
## conv.type0.997879106838302 8.639275 117.0697 1.596927

##                                 2.5 %     97.5 %
## .sig01                      0.2358847  0.6036715
## .sigma                      0.6875340  0.8881723
## (Intercept)                -0.6047807 -0.2789638
## conv.type0.997879106838302  0.6797749  1.0807176

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	-0.4419	0.08276	222.1	-5.339	0.0001	0.0001	***
conv.type0.997879106838302	0.8802	0.1015	118.1	8.676	0.0001	0.0001	***

We see a difference in DET between conversations and monologues. Specifically, we find that in conversations more function words occur in sequences than in monologues.

rNRLINE: Exploratory analyses

Raw model

# raw: does linguistic style change based on the conversation setting?
exploratory_analyses_raw_rNRLINE <- lmer(rNRLINE ~ conv.type + (1|speaker_code),
                                         data = analysis_df, REML = FALSE)

# calculate 95% CI for model
CI_exploratory_analyses_raw_rNRLINE <- confint(exploratory_analyses_raw_rNRLINE)


# calculate effect size for model
es_exploratory_analyses_raw_rNRLINE <- lme.dscore(exploratory_analyses_raw_rNRLINE,
                                                  data = analysis_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: rNRLINE ~ conv.type + (1 | speaker_code)
##    Data: analysis_df
## 
##      AIC      BIC   logLik deviance df.resid 
##   2327.1   2340.9  -1159.5   2319.1      232 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3511 -0.3017 -0.0510  0.1613  4.6574 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept)   71.02   8.427  
##  Residual                 1015.67  31.870  
## Number of obs: 236, groups:  speaker_code, 118
## 
## Fixed effects:
##              Estimate Std. Error      df t value             Pr(>|t|)    
## (Intercept)     9.172      3.035 234.996   3.022              0.00279 ** 
## conv.type0.5   72.349      4.149 118.000  17.437 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## conv.typ0.5 -0.684

##                     t       df        d
## conv.type0.5 17.36345 116.9999 3.210504

##                  2.5 %   97.5 %
## .sig01        0.000000 16.73296
## .sigma       28.198525 35.94352
## (Intercept)   3.199781 15.14425
## conv.type0.5 64.150553 80.54784

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	9.172	3.035	235	3.022	0.003	0.003	**
conv.type0.5	72.35	4.149	118	17.44	0.0001	0.0001	***

Standardized model

# standardized: does linguistic style change based on the conversation setting?
exploratory_analyses_st_rNRLINE <- lmer(rNRLINE ~ conv.type + (1|speaker_code),
                                        data = standardized_df, REML = FALSE)

# calculate 95%CI for model
CI_exploratory_analyses_st_rNRLINE <- confint(exploratory_analyses_st_rNRLINE)

# calculate effect size for model
es_exploratory_analyses_st_rNRLINE <- lme.dscore(exploratory_analyses_st_rNRLINE,
                                                 data = standardized_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: rNRLINE ~ conv.type + (1 | speaker_code)
##    Data: standardized_df
## 
##      AIC      BIC   logLik deviance df.resid 
##    489.7    503.6   -240.9    481.7      232 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3511 -0.3017 -0.0510  0.1613  4.6574 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept) 0.02952  0.1718  
##  Residual                 0.42223  0.6498  
## Number of obs: 236, groups:  speaker_code, 118
## 
## Fixed effects:
##                             Estimate Std. Error        df t value
## (Intercept)                 -0.73757    0.06187 234.99627  -11.92
## conv.type0.997879106838302   1.47514    0.08460 117.99995   17.44
##                                       Pr(>|t|)    
## (Intercept)                <0.0000000000000002 ***
## conv.type0.997879106838302 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## c.0.9978791 -0.684

##                                   t       df        d
## conv.type0.997879106838302 17.36345 116.9999 3.210504

##                                 2.5 %     97.5 %
## .sig01                      0.0000000  0.3411707
## .sigma                      0.5749438  0.7328577
## (Intercept)                -0.8593379 -0.6158004
## conv.type0.997879106838302  1.3079750  1.6423017

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	-0.7376	0.06187	235	-11.92	0.0001	0.0001	***
conv.type0.997879106838302	1.475	0.0846	118	17.44	0.0001	0.0001	***

We see a difference in NRLINE between conversations and monologues. Specifically, we find that there are more sequences of function word use in conversations than in monologues.

MaxL: Exploratory analyses

Raw model

# raw: does linguistic style change based on the conversation setting?
exploratory_analyses_raw_maxL <- lmer(maxL ~ conv.type + (1|speaker_code),
                                      data = analysis_df, REML = FALSE)

# calculate 95% CI for model
CI_exploratory_analyses_raw_maxL <- confint(exploratory_analyses_raw_maxL)


# calculate effect size for model
es_exploratory_analyses_raw_maxL <- lme.dscore(exploratory_analyses_raw_maxL,
                                               data = analysis_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: maxL ~ conv.type + (1 | speaker_code)
##    Data: analysis_df
## 
##      AIC      BIC   logLik deviance df.resid 
##   1054.4   1068.3   -523.2   1046.4      232 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7527 -0.5527 -0.0654  0.4022  7.0169 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept) 0.1275   0.3571  
##  Residual                 4.8074   2.1926  
## Number of obs: 236, groups:  speaker_code, 118
## 
## Fixed effects:
##              Estimate Std. Error       df t value            Pr(>|t|)    
## (Intercept)    5.1017     0.2045 235.8425   24.95 <0.0000000000000002 ***
## conv.type0.5   4.1441     0.2854 117.9998   14.52 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## conv.typ0.5 -0.698

##                     t  df        d
## conv.type0.5 14.45602 117 2.672919

##                 2.5 %   97.5 %
## .sig01       0.000000 1.030856
## .sigma       1.940023 2.435622
## (Intercept)  4.699237 5.504153
## conv.type0.5 3.580014 4.708122

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	5.102	0.2045	235.8	24.95	0.0001	0.0001	***
conv.type0.5	4.144	0.2854	118	14.52	0.0001	0.0001	***

Standardized model

# standardized: does linguistic style change based on the conversation setting?
exploratory_analyses_st_maxL <- lmer(maxL ~ conv.type + (1|speaker_code),
                                     data = standardized_df, REML = FALSE)

# calculate 95%CI for model
CI_exploratory_analyses_st_maxL <- confint(exploratory_analyses_st_maxL)

# calculate effect size for model
es_exploratory_analyses_st_maxL <- lme.dscore(exploratory_analyses_st_maxL,
                                              data = standardized_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: maxL ~ conv.type + (1 | speaker_code)
##    Data: standardized_df
## 
##      AIC      BIC   logLik deviance df.resid 
##    528.9    542.8   -260.5    520.9      232 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7527 -0.5527 -0.0654  0.4022  7.0169 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept) 0.01376  0.1173  
##  Residual                 0.51874  0.7202  
## Number of obs: 236, groups:  speaker_code, 118
## 
## Fixed effects:
##                             Estimate Std. Error        df t value
## (Intercept)                 -0.68063    0.06718 235.84250  -10.13
## conv.type0.997879106838302   1.36127    0.09377 117.99755   14.52
##                                       Pr(>|t|)    
## (Intercept)                <0.0000000000000002 ***
## conv.type0.997879106838302 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## c.0.9978791 -0.698

##                                   t  df        d
## conv.type0.997879106838302 14.45602 117 2.672919

##                                 2.5 %     97.5 %
## .sig01                      0.0000000  0.3386220
## .sigma                      0.6372709  0.8000683
## (Intercept)                -0.8128366 -0.5484329
## conv.type0.997879106838302  1.1759854  1.5465534

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	-0.6806	0.06718	235.8	-10.13	0.0001	0.0001	***
conv.type0.997879106838302	1.361	0.09377	118	14.52	0.0001	0.0001	***

We see a difference in maxL between conversations and monologues. Specifically, we find that the longest sequence of function word use is longer in conversations than in monologues.

L: Exploratory analyses

Raw model

# raw: does linguistic style change based on the conversation setting?
exploratory_analyses_raw_L <- lmer(L ~ conv.type + (1|speaker_code),
                                   data = analysis_df, REML = FALSE)

# calculate 95% CI for model
CI_exploratory_analyses_raw_L <- confint(exploratory_analyses_raw_L)


# calculate effect size for model
es_exploratory_analyses_raw_L <- lme.dscore(exploratory_analyses_raw_L,
                                            data = analysis_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: L ~ conv.type + (1 | speaker_code)
##    Data: analysis_df
## 
##      AIC      BIC   logLik deviance df.resid 
##    -71.8    -57.9     39.9    -79.8      232 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -11.1025  -0.3495  -0.0038   0.3568   3.5781 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept) 0.00000  0.0000  
##  Residual                 0.04176  0.2044  
## Number of obs: 236, groups:  speaker_code, 118
## 
## Fixed effects:
##               Estimate Std. Error        df t value             Pr(>|t|)
## (Intercept)    2.26881    0.01881 236.00000 120.604 < 0.0000000000000002
## conv.type0.5   0.17696    0.02660 236.00000   6.651         0.0000000002
##                 
## (Intercept)  ***
## conv.type0.5 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## conv.typ0.5 -0.707
## convergence code: 0
## boundary (singular) fit: see ?isSingular

##                     t  df         d
## conv.type0.5 6.623194 234 0.8659435

##                  2.5 %     97.5 %
## .sig01       0.0000000 0.07873474
## .sigma       0.1839970 0.22426994
## (Intercept)  2.2317901 2.30583302
## conv.type0.5 0.1246002 0.22931275

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	2.269	0.01881	236	120.6	0.0001	0.0001	***
conv.type0.5	0.177	0.0266	236	6.651	0.0001	0.0001	***

Standardized model

# standardized: does linguistic style change based on the conversation setting?
exploratory_analyses_st_L <- lmer(L ~ conv.type + (1|speaker_code),
                                  data = standardized_df, REML = FALSE)

# calculate 95%CI for model
CI_exploratory_analyses_st_L <- confint(exploratory_analyses_st_L)

# calculate effect size for model
es_exploratory_analyses_st_L <- lme.dscore(exploratory_analyses_st_L,
                                           data = standardized_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: L ~ conv.type + (1 | speaker_code)
##    Data: standardized_df
## 
##      AIC      BIC   logLik deviance df.resid 
##    636.2    650.0   -314.1    628.2      232 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -11.1025  -0.3495  -0.0038   0.3568   3.5781 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept) 0.0000   0.0000  
##  Residual                 0.8386   0.9157  
## Number of obs: 236, groups:  speaker_code, 118
## 
## Fixed effects:
##                            Estimate Std. Error       df t value
## (Intercept)                 -0.3965     0.0843 236.0000  -4.703
## conv.type0.997879106838302   0.7930     0.1192 236.0000   6.651
##                                Pr(>|t|)    
## (Intercept)                0.0000043588 ***
## conv.type0.997879106838302 0.0000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## c.0.9978791 -0.707
## convergence code: 0
## boundary (singular) fit: see ?isSingular

##                                   t  df         d
## conv.type0.997879106838302 6.623194 234 0.8659435

##                                 2.5 %     97.5 %
## .sig01                      0.0000000  0.3528307
## .sigma                      0.8245205  1.0049902
## (Intercept)                -0.5623847 -0.2305861
## conv.type0.997879106838302  0.5583537  1.0275878

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	-0.3965	0.0843	236	-4.703	0.0001	0.0001	***
conv.type0.997879106838302	0.793	0.1192	236	6.651	0.0001	0.0001	***

We see a difference in L between conversations and monologues. Specifically, we find that the average sequence length is longer in conversations than in monologues.

rENTR: Exploratory analyses

Raw model

# raw: does linguistic style change based on the conversation setting?
exploratory_analyses_raw_rENTR <- lmer(rENTR ~ conv.type + (1|speaker_code),
                                       data = analysis_df, REML = FALSE)

# calculate 95% CI for model
CI_exploratory_analyses_raw_rENTR <- confint(exploratory_analyses_raw_rENTR)


# calculate effect size for model
es_exploratory_analyses_raw_rENTR <- lme.dscore(exploratory_analyses_raw_rENTR,
                                                data = analysis_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: rENTR ~ conv.type + (1 | speaker_code)
##    Data: analysis_df
## 
##      AIC      BIC   logLik deviance df.resid 
##   -435.5   -421.7    221.7   -443.5      229 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5306 -0.5088 -0.0596  0.4028  5.7029 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept) 0.000000 0.00000 
##  Residual                 0.008727 0.09342 
## Number of obs: 233, groups:  speaker_code, 118
## 
## Fixed effects:
##                Estimate Std. Error         df t value             Pr(>|t|)
## (Intercept)    0.467234   0.008711 233.000000  53.635 < 0.0000000000000002
## conv.type0.5  -0.039887   0.012241 233.000000  -3.258              0.00129
##                 
## (Intercept)  ***
## conv.type0.5 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## conv.typ0.5 -0.712
## convergence code: 0
## boundary (singular) fit: see ?isSingular

##                      t  df          d
## conv.type0.5 -3.244427 231 -0.4269353

##                    2.5 %      97.5 %
## .sig01        0.00000000  0.03133250
## .sigma        0.08515431  0.10258872
## (Intercept)   0.45008963  0.48437903
## conv.type0.5 -0.06397911 -0.01579577

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	0.4672	0.008711	233	53.63	0.0001	0.0001	***
conv.type0.5	-0.03989	0.01224	233	-3.258	0.001	0.001	**

Standardized model

# standardized: does linguistic style change based on the conversation setting?
exploratory_analyses_st_rENTR <- lmer(rENTR ~ conv.type + (1|speaker_code),
                                      data = standardized_df, REML = FALSE)

# calculate 95%CI for model
CI_exploratory_analyses_st_rENTR <- confint(exploratory_analyses_st_rENTR)

# calculate effect size for model
es_exploratory_analyses_st_rENTR <- lme.dscore(exploratory_analyses_st_rENTR,
                                               data = standardized_df, type = "lme4")

## Linear mixed model fit by maximum likelihood . t-tests use
##   Satterthwaite's method [lmerModLmerTest]
## Formula: rENTR ~ conv.type + (1 | speaker_code)
##    Data: standardized_df
## 
##      AIC      BIC   logLik deviance df.resid 
##    657.8    671.6   -324.9    649.8      229 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5306 -0.5088 -0.0596  0.4028  5.7029 
## 
## Random effects:
##  Groups       Name        Variance Std.Dev.
##  speaker_code (Intercept) 0.0000   0.0000  
##  Residual                 0.9523   0.9759  
## Number of obs: 233, groups:  speaker_code, 118
## 
## Fixed effects:
##                            Estimate Std. Error       df t value Pr(>|t|)
## (Intercept)                  0.2110     0.0910 233.0000   2.319  0.02127
## conv.type0.997879106838302  -0.4167     0.1279 233.0000  -3.258  0.00129
##                              
## (Intercept)                * 
## conv.type0.997879106838302 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## c.0.9978791 -0.712
## convergence code: 0
## boundary (singular) fit: see ?isSingular

##                                    t  df          d
## conv.type0.997879106838302 -3.244427 231 -0.4269353

##                                  2.5 %     97.5 %
## .sig01                      0.00000000  0.3273007
## .sigma                      0.88952546  1.0716460
## (Intercept)                 0.03192105  0.3901096
## conv.type0.997879106838302 -0.66832838 -0.1650033

	Estimate	Std..Error	df	t.value	p	p_adj	sig
(Intercept)	0.211	0.091	233	2.319	0.021	0.021	*
conv.type0.997879106838302	-0.4167	0.1279	233	-3.258	0.001	0.003	**

We see a difference in rENTR between conversations and monologues. Specifically, we find that the diversity of sequences is lower in conversations than in monologues.

Post-hoc analyses

Next, we perform several post-hoc analyses to explore whether the changes observed in the planned model depend on the conversation type (i.e., personal topics versus conflict).

Again, we do so by creating a series of linear models to analyze whether conversation type---whether a personal conversation topic (P) or conflict conversation topic (K)---is connected to the amount of change in a person's language style from monologue to conversation.

Because each individual contributes only one datapoint to the planned dataset, we cannot include participant identifier as a random effect in the model. As a result, we do not include any random effects in the model.

Data preparation

First, we'll need to prepare the data by converting it from long- to wide-form.

# preparing data for Post-hoc analyses - Bring data into wide format
rqa_mon_post = rqa_mon %>%
  dplyr::rename(conv.type_m = conv.type,
                RR_m = RR,
                DET_m = DET,
                NRLINE_m = NRLINE,
                maxL_m = maxL,
                L_m = L,
                ENTR_m = ENTR,
                rENTR_m = rENTR,
                LAM_m = LAM,
                TT_m = TT,
                rNRLINE_m = rNRLINE)
rqa_conv_post = rqa_conv  %>%
  dplyr::rename(conv.type_c = conv.type,
                cond_c = cond,
                RR_c = RR,
                DET_c = DET,
                NRLINE_c = NRLINE,
                maxL_c = maxL,
                L_c = L,
                ENTR_c = ENTR,
                rENTR_c = rENTR,
                LAM_c = LAM,
                TT_c = TT,
                rNRLINE_c = rNRLINE)

# Calculate difference scores
post_hoc_df = full_join(rqa_mon_post, rqa_conv_post,
                        by=c("dyad_id", "speaker_code")) %>%
  mutate(Diff_RR = RR_m - RR_c,           # positive means higher RR in mon
         Diff_DET = DET_m - DET_c,        # positive means more DET in mon
         Diff_rNRLINE = rNRLINE_m - rNRLINE_c, # positive means more lines in monologue
         Diff_maxL = maxL_m - maxL_c, # positive means a longer  maximal line in monologue
         Diff_L = L_m - L_c, # positive means on average longer lines in monologue
         Diff_rENTR = rENTR_m - rENTR_c,  # positive means more line diversity in monologue
         Diff_LAM = LAM_m - LAM_c, # positive means proportion of points on vertical line is higher in monologue
         Diff_TT = TT_m - TT_c) %>% #positive means on average longer vertical lines in monologue
  
  
  # drop uninformative variables
  select(-conv.type_c, -conv.type_m, -cond) %>%
  
  # update coding for condition
  mutate(cond_c = as.factor(dplyr::if_else(cond_c == "P",
                                           -.5,
                                           .5)))

# save dataframe to file
write.table(post_hoc_df, './data/post_hoc_df.csv',
            sep=",", row.names=FALSE, col.names=TRUE)

# clean up what we don't need
rm(rqa_mon_post, rqa_conv_post)

We'll then go ahead and create the raw and standardized dataframes.

# standardize the analysis dataframe
post_hoc_standardized_df = post_hoc_df %>%
  
  # convert things as needed to numeric
  mutate(dyad_id = as.numeric(as.factor(dyad_id)),
         speaker_code = as.numeric(as.factor(speaker_code))) %>%
  
  # standardize
  mutate_all(funs(as.numeric(scale(as.numeric(.))))) %>%
  
  # convert to factors as needed
  mutate(dyad_id = as.factor(dyad_id),
         speaker_code = as.factor(speaker_code),
         cond_c = as.factor(cond_c))

# save dataframe to file
write.table(post_hoc_standardized_df, './data/post_hoc_standardized_df.csv',
            sep=",", row.names=FALSE, col.names=TRUE)

RR: Post-hoc analysis

Raw model

# raw: do changes in linguistic style between monologues and dialogues
#       differ by conversation type?
post_hoc_RR_raw = lm(Diff_RR ~ cond_c,
                     data = post_hoc_df)

# calculate 95% CI
CI_posthoc_RR_raw <- confint(post_hoc_RR_raw)

# calculate effect size for model
es_post_hoc_RR_raw <- cohensD(x = Diff_RR~cond_c,
                              data = post_hoc_df)

## 
## Call:
## lm(formula = Diff_RR ~ cond_c, data = post_hoc_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -23.0275  -4.1330   0.7886   4.5077  16.6707 
## 
## Coefficients:
##             Estimate Std. Error t value  Pr(>|t|)    
## (Intercept)   -1.735      0.844  -2.056    0.0421 *  
## cond_c0.5     -5.675      1.164  -4.874 0.0000035 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.316 on 116 degrees of freedom
## Multiple R-squared:   0.17,	Adjusted R-squared:  0.1628 
## F-statistic: 23.76 on 1 and 116 DF,  p-value: 0.000003496

##                 2.5 %      97.5 %
## (Intercept) -3.406464 -0.06330535
## cond_c0.5   -7.981321 -3.36918526

## [1] 0.8986007

	Estimate	Std..Error	t.value	p	sig
(Intercept)	-1.735	0.844	-2.056	0.042	*
cond_c0.5	-5.675	1.164	-4.874	0.0001	***

Standardized model

# standardized: do changes in linguistic style between monologues and dialogues
#       differ by conversation type?
post_hoc_RR_st = lm(Diff_RR ~ cond_c,
                    data = post_hoc_standardized_df)

# calculate 95% CI
CI_posthoc_RR_st <- confint(post_hoc_RR_st)

# calculate effect size for model
es_post_hoc_RR_st <- cohensD(x = Diff_RR~cond_c,
                             data = post_hoc_standardized_df)

## 
## Call:
## lm(formula = Diff_RR ~ cond_c, data = post_hoc_standardized_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3360 -0.5988  0.1143  0.6530  2.4151 
## 
## Coefficients:
##                         Estimate Std. Error t value  Pr(>|t|)    
## (Intercept)               0.4320     0.1223   3.533   0.00059 ***
## cond_c0.946346316347633  -0.8222     0.1687  -4.874 0.0000035 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.915 on 116 degrees of freedom
## Multiple R-squared:   0.17,	Adjusted R-squared:  0.1628 
## F-statistic: 23.76 on 1 and 116 DF,  p-value: 0.000003496

##                              2.5 %     97.5 %
## (Intercept)              0.1898306  0.6741604
## cond_c0.946346316347633 -1.1562695 -0.4881004

## [1] 0.8986007

	Estimate	Std..Error	t.value	p	sig
(Intercept)	0.432	0.1223	3.533	0.001	**
cond_c0.946346316347633	-0.8222	0.1687	-4.874	0.0001	***

We do see that there are significant effects in the change in the overall amount of recurrence (RR) by conversation type: The change in language style from monologues to conversation is significantly lower when having a friendly conversation about personal topics (compared to having a conflict conversation about political topics). In other words, people are less likely to change their language style when having a friendly conversation compared to an argument.

DET: Post-hoc analysis

Raw model

# raw: do changes in structure of linguistic style between monologues and dialogues
#      differ by conversation type?
post_hoc_DET_raw = lm(Diff_DET ~ cond_c,
                      data = post_hoc_df)

# calculate 95% CI
CI_posthoc_DET_raw <- confint(post_hoc_DET_raw)

# calculate effect size
es_post_hoc_DET_raw <- cohensD(x = Diff_DET~cond_c,
                               data = post_hoc_df)

## 
## Call:
## lm(formula = Diff_DET ~ cond_c, data = post_hoc_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.125  -7.420  -0.410   7.736  34.060 
## 
## Coefficients:
##             Estimate Std. Error t value  Pr(>|t|)    
## (Intercept)   -7.064      1.544  -4.575 0.0000121 ***
## cond_c0.5     -4.315      2.139  -2.018    0.0459 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.56 on 115 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.03419,	Adjusted R-squared:  0.02579 
## F-statistic: 4.071 on 1 and 115 DF,  p-value: 0.04594

##                  2.5 %      97.5 %
## (Intercept) -10.122863 -4.00525509
## cond_c0.5    -8.551501 -0.07903851

## [1] 0.373426

	Estimate	Std..Error	t.value	p	sig
(Intercept)	-7.064	1.544	-4.575	0.0001	***
cond_c0.5	-4.315	2.139	-2.018	0.046	*

Standardized model

# standardized: do changes in structure of linguistic style between monologues and dialogues
#      differ by conversation type?
post_hoc_DET_st = lm(Diff_DET ~ cond_c,
                     data = post_hoc_standardized_df)

# calculate 95% CI
CI_posthoc_DET_st <- confint(post_hoc_DET_st)

# calculate effect size
es_post_hoc_DET_st <- cohensD(x = Diff_DET~cond_c,
                              data = post_hoc_standardized_df)

## 
## Call:
## lm(formula = Diff_DET ~ cond_c, data = post_hoc_standardized_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.82930 -0.63378 -0.03499  0.66075  2.90917 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.1922     0.1319   1.457   0.1479  
## cond_c0.946346316347633  -0.3686     0.1827  -2.018   0.0459 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.987 on 115 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.03419,	Adjusted R-squared:  0.02579 
## F-statistic: 4.071 on 1 and 115 DF,  p-value: 0.04594

##                               2.5 %       97.5 %
## (Intercept)             -0.06909559  0.453425201
## cond_c0.946346316347633 -0.73040592 -0.006750885

## [1] 0.373426

	Estimate	Std..Error	t.value	p	sig
(Intercept)	0.1922	0.1319	1.457	0.148
cond_c0.946346316347633	-0.3686	0.1827	-2.018	0.046	*

We also see that there are significant effects in the change in the determinism (DET) by conversation---in other words, the structure in the patterns of recurrence. Specifically, we see that the change in language style structure from monologues to conversation is significantly lower when followed by a friendly conversation (compared to a conflict conversation). In other words, people are less likely to change the structure in their language style when having a friendly conversation as compared to an argumentative one---consistent with the results found in the post-hoc analysis of RR.

## Warning: Removed 1 rows containing missing values (geom_point).

rNRLINE: Post-hoc analysis

Raw model

# raw: do changes in amount of structure of linguistic style between
#      monologues and dialogues differ by conversation type?
post_hoc_rNRLINE_raw = lm(Diff_rNRLINE ~ cond_c,
                          data = post_hoc_df)

# calculate 95% CI
CI_posthoc_rNRLINE_raw <- confint(post_hoc_rNRLINE_raw)

# calculate effect size
es_post_hoc_rNRLINE_raw <- cohensD(x = Diff_rNRLINE~cond_c,
                                   data = post_hoc_df)

## 
## Call:
## lm(formula = Diff_rNRLINE ~ cond_c, data = post_hoc_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -152.12  -22.44   11.07   30.62   76.78 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  -68.932      6.059 -11.377 <0.0000000000000002 ***
## cond_c0.5     -6.504      8.358  -0.778               0.438    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 45.34 on 116 degrees of freedom
## Multiple R-squared:  0.005193,	Adjusted R-squared:  -0.003383 
## F-statistic: 0.6055 on 1 and 116 DF,  p-value: 0.4381

##                 2.5 %    97.5 %
## (Intercept) -80.93178 -56.93182
## cond_c0.5   -23.05894  10.05078

## [1] 0.1434544

	Estimate	Std..Error	t.value	p	sig
(Intercept)	-68.93	6.059	-11.38	0.0001	***
cond_c0.5	-6.504	8.358	-0.7781	0.44

Standardized model

# standardized: do changes in amount of structure of linguistic style between
#      monologues and dialogues differ by conversation type?
post_hoc_rNRLINE_st = lm(Diff_rNRLINE ~ cond_c,
                         data = post_hoc_standardized_df)

# calculate 95% CI
CI_posthoc_rNRLINE_st <- confint(post_hoc_rNRLINE_st)

# calculate effect size
es_post_hoc_rNRLINE_st <- cohensD(x = Diff_rNRLINE~cond_c,
                                  data = post_hoc_standardized_df)

## 
## Call:
## lm(formula = Diff_rNRLINE ~ cond_c, data = post_hoc_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -152.12  -22.44   11.07   30.62   76.78 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  -68.932      6.059 -11.377 <0.0000000000000002 ***
## cond_c0.5     -6.504      8.358  -0.778               0.438    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 45.34 on 116 degrees of freedom
## Multiple R-squared:  0.005193,	Adjusted R-squared:  -0.003383 
## F-statistic: 0.6055 on 1 and 116 DF,  p-value: 0.4381

##                              2.5 %    97.5 %
## (Intercept)             -0.1896179 0.3406214
## cond_c0.946346316347633 -0.5094491 0.2220554

## [1] 0.1434544

	Estimate	Std..Error	t.value	p	sig
(Intercept)	0.0755	0.1339	0.5641	0.57
cond_c0.946346316347633	-0.1437	0.1847	-0.7781	0.44

In contrast with RR and DET, we do not see a difference in the normalized number of lines on the RP by conversation type. Essentially, this is another measure of continued structure within a system by capturing the total number of lines in the plot.

MaxL: Post-hoc analysis

Raw model

# raw: do changes in uniformity of structure of linguistic style between monologues
#       and dialogues differ by conversation type?
post_hoc_maxL_raw = lm(Diff_maxL ~ cond_c,
                       data = post_hoc_df)

# calculate 95% CI
CI_posthoc_maxL_raw <- confint(post_hoc_maxL_raw)

# calculate effect size
es_post_hoc_maxL_raw <- cohensD(x = Diff_maxL~cond_c,
                                data = post_hoc_standardized_df)

## 
## Call:
## lm(formula = Diff_maxL ~ cond_c, data = post_hoc_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -16.2581  -1.2581  -0.2581   1.7419   6.7419 
## 
## Coefficients:
##             Estimate Std. Error t value           Pr(>|t|)    
## (Intercept)  -3.4821     0.4092  -8.509 0.0000000000000714 ***
## cond_c0.5    -1.2598     0.5646  -2.231             0.0276 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.062 on 116 degrees of freedom
## Multiple R-squared:  0.04116,	Adjusted R-squared:  0.03289 
## F-statistic: 4.979 on 1 and 116 DF,  p-value: 0.02757

##                 2.5 %     97.5 %
## (Intercept) -4.292664 -2.6716212
## cond_c0.5   -2.377968 -0.1416177

## [1] 0.4113798

	Estimate	Std..Error	t.value	p	sig
(Intercept)	-3.482	0.4092	-8.509	0.0001	***
cond_c0.5	-1.26	0.5646	-2.231	0.028	*

Standardized model

# standardized: do changes in uniformity of structure of linguistic style between monologues
#       and dialogues differ by conversation type?
post_hoc_maxL_st = lm(Diff_maxL ~ cond_c,
                      data = post_hoc_standardized_df)

# calculate 95% CI
CI_posthoc_maxL_st <- confint(post_hoc_maxL_st)

# calculate effect size
es_post_hoc_maxL_st <- cohensD(x = Diff_maxL~cond_c,
                               data = post_hoc_standardized_df)

## 
## Call:
## lm(formula = Diff_maxL ~ cond_c, data = post_hoc_standardized_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2210 -0.4040 -0.0829  0.5594  2.1650 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.2126     0.1314   1.618   0.1085  
## cond_c0.946346316347633  -0.4046     0.1813  -2.231   0.0276 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9834 on 116 degrees of freedom
## Multiple R-squared:  0.04116,	Adjusted R-squared:  0.03289 
## F-statistic: 4.979 on 1 and 116 DF,  p-value: 0.02757

##                               2.5 %      97.5 %
## (Intercept)             -0.04771886  0.47284684
## cond_c0.946346316347633 -0.76363685 -0.04547769

## [1] 0.4113798

	Estimate	Std..Error	t.value	p	sig
(Intercept)	0.2126	0.1314	1.618	0.108
cond_c0.946346316347633	-0.4046	0.1813	-2.231	0.028	*

Here, we see that there are significant effects in the change in the longest diagonal line segment (maxL). Specifically, we see that the change in sequential patterns of function word use is significantly lower when followed by a friendly conversation. Put together, the change in the longest sequence of function words is smaller when having a friendly conversation as compared to an argumentative one.

L: Post-hoc analysis

Raw model

# raw: do changes in uniformity of structure of linguistic style between monologues
#       and dialogues differ by conversation type?
post_hoc_L_raw = lm(Diff_L ~ cond_c,
                    data = post_hoc_df)

# calculate 95% CI
CI_posthoc_L_raw <- confint(post_hoc_L_raw)

# calculate effect size
es_post_hoc_L_raw <- cohensD(x = Diff_L~cond_c,
                             data = post_hoc_standardized_df)

## 
## Call:
## lm(formula = Diff_L ~ cond_c, data = post_hoc_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.27645 -0.11972 -0.01486  0.11788  0.85480 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.11199    0.03888  -2.880  0.00473 **
## cond_c0.5   -0.12364    0.05364  -2.305  0.02294 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.291 on 116 degrees of freedom
## Multiple R-squared:  0.0438,	Adjusted R-squared:  0.03556 
## F-statistic: 5.313 on 1 and 116 DF,  p-value: 0.02294

##                  2.5 %      97.5 %
## (Intercept) -0.1890006 -0.03498456
## cond_c0.5   -0.2298792 -0.01740267

## [1] 0.4249471

	Estimate	Std..Error	t.value	p	sig
(Intercept)	-0.112	0.03888	-2.88	0.005	**
cond_c0.5	-0.1236	0.05364	-2.305	0.023	*

Standardized model

# standardized: do changes in uniformity of structure of linguistic style between monologues
#       and dialogues differ by conversation type?
post_hoc_L_st = lm(Diff_L ~ cond_c,
                   data = post_hoc_standardized_df)

# calculate 95% CI
CI_posthoc_L_st <- confint(post_hoc_L_st)

# calculate effect size
es_post_hoc_L_st <- cohensD(x = Diff_L~cond_c,
                            data = post_hoc_standardized_df)

## 
## Call:
## lm(formula = Diff_L ~ cond_c, data = post_hoc_standardized_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.6837 -0.4041 -0.0501  0.3979  2.8852 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.2193     0.1312   1.671   0.0974 .
## cond_c0.946346316347633  -0.4173     0.1810  -2.305   0.0229 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9821 on 116 degrees of freedom
## Multiple R-squared:  0.0438,	Adjusted R-squared:  0.03556 
## F-statistic: 5.313 on 1 and 116 DF,  p-value: 0.02294

##                              2.5 %      97.5 %
## (Intercept)             -0.0406524  0.47919648
## cond_c0.946346316347633 -0.7759093 -0.05873907

## [1] 0.4249471

	Estimate	Std..Error	t.value	p	sig
(Intercept)	0.2193	0.1312	1.671	0.097	.
cond_c0.946346316347633	-0.4173	0.181	-2.305	0.023	*

We see that there are significant effects in the change in the average line length (L). Specifically, we see that the change in average line length (sequences of uninterrupted function word use) is significantly lower when followed by a friendly conversation. In other words, the change in the average uninterrupted sequence of function word use is smaller when having a friendly conversation as compared to an argumentative one.

rENTR: Post-hoc analysis

Raw model

# raw: do changes in uniformity of structure of linguistic style between monologues
#       and dialogues differ by conversation type?
post_hoc_rENTR_raw = lm(Diff_rENTR ~ cond_c,
                        data = post_hoc_df)

# calculate 95% CI
CI_posthoc_rENTR_raw <- confint(post_hoc_rENTR_raw)

# calculate effect size
es_post_hoc_rENTR_raw <- cohensD(x = Diff_rENTR~cond_c,
                                 data = post_hoc_standardized_df)

## 
## Call:
## lm(formula = Diff_rENTR ~ cond_c, data = post_hoc_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.30906 -0.08633 -0.01154  0.06998  0.60921 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.06499    0.01866   3.484 0.000705 ***
## cond_c0.5   -0.04670    0.02583  -1.808 0.073249 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1384 on 113 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.02812,	Adjusted R-squared:  0.01952 
## F-statistic: 3.269 on 1 and 113 DF,  p-value: 0.07325

##                   2.5 %      97.5 %
## (Intercept)  0.02802731 0.101949081
## cond_c0.5   -0.09786996 0.004470106

## [1] 0.3375332

	Estimate	Std..Error	t.value	p	sig
(Intercept)	0.06499	0.01866	3.484	0.001	**
cond_c0.5	-0.0467	0.02583	-1.808	0.073	.

Standardized model

# standardized: do changes in uniformity of structure of linguistic style between monologues
#       and dialogues differ by conversation type?
post_hoc_rENTR_st = lm(Diff_rENTR ~ cond_c,
                       data = post_hoc_standardized_df)

# calculate 95% CI
CI_posthoc_rENTR_st <- confint(post_hoc_rENTR_st)

# calculate effect size
es_post_hoc_rENTR_st <- cohensD(x = Diff_rENTR~cond_c,
                                data = post_hoc_standardized_df)

## 
## Call:
## lm(formula = Diff_rENTR ~ cond_c, data = post_hoc_standardized_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2119 -0.6178 -0.0826  0.5009  4.3600 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.1744     0.1335   1.306   0.1942  
## cond_c0.946346316347633  -0.3342     0.1848  -1.808   0.0732 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9902 on 113 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.02812,	Adjusted R-squared:  0.01952 
## F-statistic: 3.269 on 1 and 113 DF,  p-value: 0.07325

##                               2.5 %     97.5 %
## (Intercept)             -0.09014526 0.43889978
## cond_c0.946346316347633 -0.70043793 0.03199176

## [1] 0.3375332

	Estimate	Std..Error	t.value	p	sig
(Intercept)	0.1744	0.1335	1.306	0.194
cond_c0.946346316347633	-0.3342	0.1848	-1.808	0.073	.

Unlike RR and DET, we do not see a difference in normalized entropy by conversation type. Normalized entropy essentially captures the degree to which the structure of the line lengths are uniform (i.e., lower variety in line lengths means lower rENTR) or more heterogenous (i.e., higher variety in line lengths means higher rENTR).

## Warning: Removed 3 rows containing missing values (geom_point).

Files

beyond_consistency.md

Latest commit

History

beyond_consistency.md

File metadata and controls

Preliminaries

Data preparation

Recurrence quantification analysis

Monologues

Conversations

Proportions

Monologues

Conversations

Create dataframes

Data analysis

Power Sensitivity Analyses

Power analyses for d = .4

Power analyses for d = .3

Additional Power analyses d = .29 to see if the power is below 80%

Power analyses for d = .2

Power analyses for d = .1

Descriptives and correlation tables

Planned analysis

RR: Raw model

RR: Standardized model

Validating RR against proportion

Raw model

Standardized model

Additional exploratory analyses

DET: Exploratory analyses

Raw model

Standardized model

rNRLINE: Exploratory analyses

Raw model

Standardized model

MaxL: Exploratory analyses

Raw model

Standardized model

L: Exploratory analyses

Raw model

Standardized model

rENTR: Exploratory analyses

Raw model

Standardized model

Post-hoc analyses

Data preparation

RR: Post-hoc analysis

Raw model

Standardized model

DET: Post-hoc analysis

Raw model

Standardized model

rNRLINE: Post-hoc analysis

Raw model

Standardized model

MaxL: Post-hoc analysis

Raw model

Standardized model

L: Post-hoc analysis

Raw model

Standardized model

rENTR: Post-hoc analysis

Raw model

Standardized model