Loading packages
library(tidyverse)## ── Attaching packages ─────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 2.2.1 ✔ purrr 0.2.4
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## ✔ tidyr 0.8.0 ✔ stringr 1.2.0
## ✔ readr 1.1.1 ✔ forcats 0.2.0## Warning: package 'tibble' was built under R version 3.4.3## Warning: package 'tidyr' was built under R version 3.4.3## ── Conflicts ────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(jmRtools)Reading in data
LIWC <- read_csv("LIWC_Results_Revised.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## Module = col_integer(),
## Part = col_integer(),
## SyncvAsync = col_character(),
## Group = col_integer(),
## Message = col_character(),
## WC = col_integer()
## )## See spec(...) for full column specifications.## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)## Warning: 7 parsing failures.
## row # A tibble: 5 x 5 col row col expected actual file expected <int> <chr> <chr> <chr> <chr> actual 1 2207 Part an integer ! 'LIWC_Results_Revised.csv' file 2 2208 Part an integer ! 'LIWC_Results_Revised.csv' row 3 2209 Part an integer ! 'LIWC_Results_Revised.csv' col 4 2210 Part an integer ! 'LIWC_Results_Revised.csv' expected 5 2211 Part an integer ! 'LIWC_Results_Revised.csv'
## ... ................. ... .......................................................... ........ .......................................................... ...... .......................................................... .... .......................................................... ... .......................................................... ... .......................................................... ........ ..........................................................
## See problems(...) for more details.#View(LIWC)MANOVA of variables of interest Looks like affect, posemo, and certain are significant (achieve is slightly significant)
fit<-manova(cbind(LIWC$Tone, LIWC$affect, LIWC$posemo, LIWC$negemo, LIWC$anx, LIWC$achieve, LIWC$affiliation, LIWC$discrep, LIWC$tentat, LIWC$certain) ~ LIWC$SyncvAsync, data = LIWC)
summary(fit, test="Pillai")## Df Pillai approx F num Df den Df Pr(>F)
## LIWC$SyncvAsync 1 0.012521 5.7149 10 4507 1.428e-08 ***
## Residuals 4516
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary.aov(fit)## Response 1 :
## Df Sum Sq Mean Sq F value Pr(>F)
## LIWC$SyncvAsync 1 892 891.71 0.6444 0.4222
## Residuals 4516 6249226 1383.80
##
## Response 2 :
## Df Sum Sq Mean Sq F value Pr(>F)
## LIWC$SyncvAsync 1 6788 6788.3 26.082 3.408e-07 ***
## Residuals 4516 1175379 260.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response 3 :
## Df Sum Sq Mean Sq F value Pr(>F)
## LIWC$SyncvAsync 1 6088 6087.7 24.024 9.846e-07 ***
## Residuals 4516 1144345 253.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response 4 :
## Df Sum Sq Mean Sq F value Pr(>F)
## LIWC$SyncvAsync 1 27 27.250 1.6344 0.2012
## Residuals 4516 75295 16.673
##
## Response 5 :
## Df Sum Sq Mean Sq F value Pr(>F)
## LIWC$SyncvAsync 1 0 0.00106 4e-04 0.9833
## Residuals 4516 10916 2.41724
##
## Response 6 :
## Df Sum Sq Mean Sq F value Pr(>F)
## LIWC$SyncvAsync 1 111 111.04 5.0613 0.02451 *
## Residuals 4516 99079 21.94
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response 7 :
## Df Sum Sq Mean Sq F value Pr(>F)
## LIWC$SyncvAsync 1 39 39.323 0.4589 0.4982
## Residuals 4516 386983 85.692
##
## Response 8 :
## Df Sum Sq Mean Sq F value Pr(>F)
## LIWC$SyncvAsync 1 17 16.994 0.8216 0.3648
## Residuals 4516 93409 20.684
##
## Response 9 :
## Df Sum Sq Mean Sq F value Pr(>F)
## LIWC$SyncvAsync 1 20 19.996 0.5698 0.4504
## Residuals 4516 158489 35.095
##
## Response 10 :
## Df Sum Sq Mean Sq F value Pr(>F)
## LIWC$SyncvAsync 1 853 852.64 10.24 0.001384 **
## Residuals 4516 376040 83.27
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Looking at how many sync vs async responses we have (pretty skewed number)
table(LIWC$SyncvAsync)##
## A S
## 780 3738Creating two datasets for descriptives of sync vs async
Sync_LIWC <- LIWC %>%
filter(SyncvAsync == "S")
ASync_LIWC <- LIWC %>%
filter(SyncvAsync == "A")Descriptives of significant difference variables for sync and async Affect
psych::describe(Sync_LIWC$affect)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3738 9.91 16.95 4.17 6 6.18 0 100 100 3.15 12.03
## se
## X1 0.28psych::describe(ASync_LIWC$affect)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 780 6.67 11.39 4.04 4.43 5.99 0 100 100 4.81 31.25
## se
## X1 0.41Posemo
psych::describe(Sync_LIWC$posemo)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 3738 8.77 16.72 1.48 4.78 2.19 0 100 100 3.38 13.55
## se
## X1 0.27psych::describe(ASync_LIWC$posemo)## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 780 5.7 11.32 2.65 3.36 3.93 0 100 100 5.09 33.75
## se
## X1 0.41Certain
psych::describe(Sync_LIWC$certain)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3738 2.93 9.75 0 0.9 0 0 100 100 6.99 59.85 0.16psych::describe(ASync_LIWC$certain)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 780 1.78 5.15 0 0.89 0 0 100 100 11.44 188.08 0.18Achievement
psych::describe(Sync_LIWC$achieve)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 3738 1.81 4.66 0 0.59 0 0 50 50 3.88 20.01 0.08psych::describe(ASync_LIWC$achieve)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 780 2.22 4.8 0 1.21 0 0 66.67 66.67 5.84 57.97 0.17