CIG_analysis

Setup, find files

# Load the packages

library(rstatix)

Attaching package: 'rstatix'

The following object is masked from 'package:stats':

    filter

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks rstatix::filter(), stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(knitr)
library(ggpubr)
library(readxl)
library(stringr)
library(lavaan)

This is lavaan 0.6-19
lavaan is FREE software! Please report any bugs.

library(corrplot)

corrplot 0.95 loaded

library(lm.beta)
library(ggsci)
library(svglite)
library(emmeans)

Welcome to emmeans.
Caution: You lose important information if you filter this package's results.
See '? untidy'

nov_cols <- c("Cameron_Novelty", "kaelyn_novelty", "Cameron_Cameron","Cameron _Novelty")

files <- list.files(path="allfiles", full.names = TRUE)

Do joining and merging

CIG_merge <- read_csv(files[1], col_types = cols(session.ended = col_character())) %>%
  select(-c(ends_with("notes"), title, body, id, vote.index, contains("direction"), 
            contains("reply_to_id"), contains("cast")))%>%
  rename_with(~ "novelty" , any_of(nov_cols))

session_names <- CIG_merge$session.name[1]

for (i in 2:length(files)){
  # for (i in 2:50){
  
  #get the session name of the current file, see if it's in the dataset already
  tempdat <- read_csv(files[i], col_types = cols(session.ended = col_character()), show_col_types = FALSE)
  temp_session <- tempdat$session.name[1]
  
  if (temp_session %in% session_names){
    # print(i)
    tempdat <- read_csv(files[i], col_types = cols(session.ended = col_character()), show_col_types = FALSE) %>%
      select(-c(ends_with("notes"), title, body, id, vote.index, contains("direction"), 
                contains("reply_to_id"), contains("cast"))) %>%
      rename_with(~ paste0("novelty",i) , any_of(nov_cols))
    CIG_merge <- full_join(CIG_merge, tempdat)
  } else {
    tempdat <- read_csv(files[i], col_types = cols(session.ended = col_character()), show_col_types = FALSE) %>%
      select(-c(ends_with("notes"), title, body, id, vote.index, contains("direction"), 
                contains("reply_to_id"), contains("cast"))) %>%
      rename_with(~ "novelty" , any_of(nov_cols))
    CIG_merge <- bind_rows(CIG_merge, tempdat)
    
    session_names <- c(session_names, temp_session)
  }
  
}

Average ratings where there are two raters, combine I or E columns

CIG_merge_averaged <- CIG_merge %>%
  mutate(nov_avg = rowMeans(across(contains("novelty")), na.rm = T),
         nraters = rowSums(!is.na(across(contains("novelty")))), .keep = "unused")

CIG_merge_averaged <- CIG_merge_averaged %>%
  tidyr::unite(col = "i_e",
               Anakaren_idea_or_elab, kaelyn_idea_or_elab, kaelyn_kaelyn_idea_or_elab,
               remove = T,na.rm = TRUE)

Summarize, do stats

session_nov_summary <- CIG_merge_averaged %>%
  filter(i_e %in% c("i", "e")) %>% 
  group_by(session.name, condition.name) %>%
  summarize(
    avg_nov_ideas = mean(nov_avg[i_e == "i"], na.rm = TRUE),
    avg_nov_elabs = mean(nov_avg[i_e == "e"], na.rm = TRUE),
    total_ideas = sum(i_e == "i", na.rm = TRUE),
    total_elabs = sum(i_e == "e", na.rm = TRUE),
    nraters = first(nraters),
    .groups = "drop"
  )

session_nov_summary <- session_nov_summary %>%
  mutate(Feedback = case_when(
    grepl("F", condition.name) ~ "Feedback",
    .default = "No Feedback"
  ),
  Two_or_three = case_when(
    grepl("2", condition.name) ~ "Linear",
    .default = "Cyclical"
  ))

anova_ideas <- anova_test(avg_nov_ideas ~ Feedback * Two_or_three, 
                          data = session_nov_summary, 
                          type = 3)
anova_ideas

ANOVA Table (type III tests)

                 Effect DFn DFd         F     p p<.05      ges
1              Feedback   1  81 3.370e-01 0.563       4.00e-03
2          Two_or_three   1  81 3.226e+00 0.076       3.80e-02
3 Feedback:Two_or_three   1  81 1.440e-08 1.000       1.78e-10

nov_ideas_plot <- ggline(data = session_nov_summary, 
                         x= "Feedback", 
                         y = "avg_nov_ideas", 
                         color = "Two_or_three", 
                         add = "mean_ci",
                         palette = "jco",
                         ylab = "Average Novelty of Ideas",
                         size =1.25,
                         legend = c(.9,.8),
                         legend.title = "",
                         ylim = c(1,5),
                         xlim = c(1.25,1.75),
                         position = position_dodge(width = .025))

nov_ideas_plot

ggsave("plots/nov_ideas_plot.svg", nov_ideas_plot, dpi = 600, width = 5, height = 5)


anova_elabs <- anova_test(avg_nov_elabs ~ Feedback * Two_or_three, 
                          data = session_nov_summary, 
                          type = 3)

Warning: NA detected in rows: 8,10,13,15,21,22,23,24,25,30,31,33,34,35,36,37,39,40,42,43,44,45,47,48,49,50,53,54,57,61,62,63,64,65,66,67,68,70,71,72,75,76,81,83,85.
Removing this rows before the analysis.

anova_elabs

ANOVA Table (type III tests)

                 Effect DFn DFd     F     p p<.05   ges
1              Feedback   1  36 2.893 0.098       0.074
2          Two_or_three   1  36 3.105 0.087       0.079
3 Feedback:Two_or_three   1  36 1.014 0.321       0.027

nov_elab_plot <- ggline(data = session_nov_summary, 
                        x= "Feedback", 
                        y = "avg_nov_elabs", 
                        color = "Two_or_three", 
                        add = "mean_ci",
                        palette = "jco",
                        ylab = "Average Novelty of Elaborations",
                        size =1.25,
                        legend = c(.9,.8),
                        legend.title = "",
                        ylim = c(1,5),
                        xlim = c(1.25,1.75),
                        position = position_dodge(width = .025))
nov_elab_plot

Warning: Removed 45 rows containing non-finite outside the scale range
(`stat_summary()`).

ggsave("plots/nov_elab_plot.svg", nov_elab_plot, dpi = 600, width = 5, height = 5)

Warning: Removed 45 rows containing non-finite outside the scale range
(`stat_summary()`).

anova_countideas <- anova_test(total_ideas ~ Feedback * Two_or_three, 
                               data = session_nov_summary, 
                               type = 3)
anova_countideas

ANOVA Table (type III tests)

                 Effect DFn DFd     F     p p<.05   ges
1              Feedback   1  81 0.375 0.542       0.005
2          Two_or_three   1  81 0.121 0.729       0.001
3 Feedback:Two_or_three   1  81 0.134 0.716       0.002

idea_count_plot <- ggline(data = session_nov_summary, 
                          x= "Feedback", 
                          y = "total_ideas", 
                          color = "Two_or_three", 
                          add = "mean_ci",
                          palette = "jco",
                          ylab = "Average Number of Ideas",
                          size =1.25,
                          legend = c(.924,.95),
                          legend.title = "",
                          ylim = c(0,50),
                          xlim = c(1.25,1.75),
                          position = position_dodge(width = .025))
idea_count_plot

ggsave("plots/idea_count_plot.svg", idea_count_plot, dpi = 600, width = 5, height = 5)

anova_countelabs <- anova_test(total_elabs ~ Feedback * Two_or_three, 
                               data = session_nov_summary, 
                               type = 3)
anova_countelabs

ANOVA Table (type III tests)

                 Effect DFn DFd     F     p p<.05      ges
1              Feedback   1  81 0.015 0.903       0.000183
2          Two_or_three   1  81 2.195 0.142       0.026000
3 Feedback:Two_or_three   1  81 0.497 0.483       0.006000

elab_count_plot <- ggline(data = session_nov_summary, 
                          x= "Feedback", 
                          y = "total_elabs", 
                          color = "Two_or_three", 
                          add = "mean_ci",
                          palette = "jco",
                          ylab = "Average Number of Elaborations",
                          size = 1.25,
                          legend = c(.924,.8),
                          legend.title = "",
                          ylim = c(0,10),
                          xlim = c(1.25,1.75),
                          position = position_dodge(width = .025))
elab_count_plot

ggsave("plots/elab_count_plot.svg", elab_count_plot, dpi = 600, width = 5, height = 5)

Bring in the final plan file

final_plan_ratings <- read_xlsx("final_ratings.xlsx") %>%
  select(session:word_count) %>%
  filter(!is.na(session)) %>%
  select(-`final plan`)

New names:
• `` -> `...8`
• `` -> `...9`
• `` -> `...10`
• `` -> `...11`
• `` -> `...12`
• `` -> `...13`
• `` -> `...14`
• `` -> `...15`
• `` -> `...16`
• `` -> `...17`
• `` -> `...18`
• `` -> `...19`
• `` -> `...20`
• `` -> `...21`
• `` -> `...22`
• `` -> `...23`
• `` -> `...24`
• `` -> `...25`
• `` -> `...26`
• `` -> `...27`
• `` -> `...28`
• `` -> `...29`
• `` -> `...30`
• `` -> `...31`
• `` -> `...32`

#rename columns so they don't get mixed up after merging
colnames(final_plan_ratings)[2:5] <- paste0("final_plan_", colnames(final_plan_ratings)[2:5])

Figure out conditions for the extra final plans

final_plan_ratings <- final_plan_ratings %>%
  mutate(Feedback = case_when(
    str_sub(session, -1) == "f" ~ "Feedback",
    .default = "No Feedback"),
    Two_or_three = case_when(
      grepl("_c2", session) ~ "Linear",
      .default = "Cyclical"
      
    ))

Merge final plan ratings with summarized stats above

#fix a session name
session_nov_summary$session.name[46] <- "cig_250212_12pm_3p_c2f"

combined_cig_ratings <- full_join(session_nov_summary, final_plan_ratings, by = join_by(session.name == session, Feedback, Two_or_three))

write.csv(combined_cig_ratings, file = "combined_cig_ratings.csv", row.names = F)

some analyses with final plans

anova_FinThorough <- anova_test(final_plan_Thoroughness_ ~ Feedback * Two_or_three, 
                                data = combined_cig_ratings, 
                                type = 3)

anova_FinThorough

ANOVA Table (type III tests)

                 Effect DFn DFd     F     p p<.05   ges
1              Feedback   1  98 0.953 0.331       0.010
2          Two_or_three   1  98 5.060 0.027     * 0.049
3 Feedback:Two_or_three   1  98 5.743 0.018     * 0.055

ggline(data = combined_cig_ratings, 
       x= "Feedback", 
       y = "final_plan_Thoroughness_", 
       color = "Two_or_three", 
       add = "mean_ci")

anova_FinNov <- anova_test(final_plan_Novelty_ ~ Feedback * Two_or_three, 
                           data = combined_cig_ratings, 
                           type = 3)

anova_FinNov

ANOVA Table (type III tests)

                 Effect DFn DFd     F     p p<.05      ges
1              Feedback   1  98 0.004 0.949       4.13e-05
2          Two_or_three   1  98 3.880 0.052       3.80e-02
3 Feedback:Two_or_three   1  98 4.910 0.029     * 4.80e-02

ggline(data = combined_cig_ratings, 
       x= "Feedback", 
       y = "final_plan_Novelty_", 
       color = "Two_or_three", 
       add = "mean_se")

anova_FinComplex <- anova_test(final_plan_Complexity_ ~ Feedback * Two_or_three, 
                               data = combined_cig_ratings, 
                               type = 3)

anova_FinComplex

ANOVA Table (type III tests)

                 Effect DFn DFd     F     p p<.05   ges
1              Feedback   1  98 0.284 0.595       0.003
2          Two_or_three   1  98 3.668 0.058       0.036
3 Feedback:Two_or_three   1  98 5.492 0.021     * 0.053

ggline(data = combined_cig_ratings, 
       x= "Feedback", 
       y = "final_plan_Complexity_", 
       color = "Two_or_three", 
       add = "mean_se")

anova_FinWords <- anova_test(final_plan_word_count ~ Feedback * Two_or_three, 
                             data = combined_cig_ratings, 
                             type = 3)

anova_FinWords

ANOVA Table (type III tests)

                 Effect DFn DFd     F     p p<.05   ges
1              Feedback   1  98 1.792 0.184       0.018
2          Two_or_three   1  98 0.828 0.365       0.008
3 Feedback:Two_or_three   1  98 3.176 0.078       0.031

ggline(data = combined_cig_ratings, 
       x= "Feedback", 
       y = "final_plan_word_count", 
       color = "Two_or_three", 
       add = "mean_se")

corrs <- combined_cig_ratings %>% 
  select(starts_with("final")) %>%
  cor

ps <- combined_cig_ratings %>% 
  select(starts_with("final")) %>%
  cor.mtest

corrplot(corrs, 
         p.mat = ps$p, 
         sig.level = c(0.001, 0.01, 0.05), 
         insig = 'label_sig', 
         pch.cex = .95)

# Specify and fit the CFA model
# model1 = '
# one_DV =~ final_plan_word_count + final_plan_Complexity_ 
# + final_plan_Novelty_ + final_plan_Thoroughness_
# '
# fit_model1 = cfa(model = model1, data = combined_cig_ratings)
# summary(fit_model1, fit.measures = T, standardized = T)
# 
# modificationindices(fit_model1, sort. = T)

model2 = '
thocount =~ final_plan_word_count  + final_plan_Thoroughness_
novcomp =~ final_plan_Novelty_ + final_plan_Complexity_
'
fit_model2 = cfa(model = model2, data = combined_cig_ratings)

Warning: lavaan->lav_data_full():  
   some observed variances are (at least) a factor 1000 times larger than 
   others; use varTable(fit) to investigate

Warning: lavaan->lav_object_post_check():  
   some estimated ov variances are negative

summary(fit_model2, fit.measures = T, standardized = T)

lavaan 0.6-19 ended normally after 334 iterations

  Estimator                                         ML
  Optimization method                           NLMINB
  Number of model parameters                         9

  Number of observations                           102

Model Test User Model:
                                                      
  Test statistic                                 0.018
  Degrees of freedom                                 1
  P-value (Chi-square)                           0.893

Model Test Baseline Model:

  Test statistic                               159.130
  Degrees of freedom                                 6
  P-value                                        0.000

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    1.000
  Tucker-Lewis Index (TLI)                       1.038

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)               -966.142
  Loglikelihood unrestricted model (H1)       -966.133
                                                      
  Akaike (AIC)                                1950.285
  Bayesian (BIC)                              1973.910
  Sample-size adjusted Bayesian (SABIC)       1945.482

Root Mean Square Error of Approximation:

  RMSEA                                          0.000
  90 Percent confidence interval - lower         0.000
  90 Percent confidence interval - upper         0.122
  P-value H_0: RMSEA <= 0.050                    0.906
  P-value H_0: RMSEA >= 0.080                    0.077

Standardized Root Mean Square Residual:

  SRMR                                           0.002

Parameter Estimates:

  Standard errors                             Standard
  Information                                 Expected
  Information saturated (h1) model          Structured

Latent Variables:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  thocount =~                                                           
    fnl_pln_wrd_cn    1.000                              44.361    0.677
    fnl_pln_Thrgh_    0.026    0.004    6.090    0.000    1.167    1.026
  novcomp =~                                                            
    fnl_pln_Nvlty_    1.000                               0.698    0.625
    fnl_pln_Cmplx_    1.424    0.261    5.459    0.000    0.994    0.850

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  thocount ~~                                                           
    novcomp          22.691    6.554    3.462    0.001    0.733    0.733

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .fnl_pln_wrd_cn 2321.197  406.126    5.715    0.000 2321.197    0.541
   .fnl_pln_Thrgh_   -0.068    0.169   -0.405    0.685   -0.068   -0.053
   .fnl_pln_Nvlty_    0.758    0.127    5.973    0.000    0.758    0.609
   .fnl_pln_Cmplx_    0.380    0.151    2.520    0.012    0.380    0.278
    thocount       1967.915  560.679    3.510    0.000    1.000    1.000
    novcomp           0.487    0.155    3.146    0.002    1.000    1.000

modificationindices(fit_model2, sort. = T)

                        lhs op                    rhs    mi    epc sepc.lv
18    final_plan_word_count ~~ final_plan_Complexity_ 0.018 -0.867  -0.867
17    final_plan_word_count ~~    final_plan_Novelty_ 0.018  0.609   0.609
19 final_plan_Thoroughness_ ~~    final_plan_Novelty_ 0.018 -0.016  -0.016
20 final_plan_Thoroughness_ ~~ final_plan_Complexity_ 0.018  0.023   0.023
   sepc.all sepc.nox
18   -0.029   -0.029
17    0.015    0.015
19   -0.070   -0.070
20    0.141    0.141

# modelnonov = '
# thorcompcount =~ final_plan_word_count + final_plan_Complexity_ + final_plan_Thoroughness_
# '
# 
# fit_model3 = cfa(model = modelnonov, data = combined_cig_ratings)
# summary(fit_model3, fit.measures = T, standardized = T)
# 
# modificationindices(fit_model3, sort. = T)

Averaging and anova + regressions

combined_cig_ratings <- combined_cig_ratings %>%
  mutate(zcount = scale(final_plan_word_count),
         zthor = scale(final_plan_Thoroughness_))

combined_cig_ratings <- combined_cig_ratings %>%
  mutate(zcount_thor = (zcount + zthor)/2,
         compnov = (final_plan_Novelty_ + final_plan_Complexity_)/2)

# combined_cig_ratings <- combined_cig_ratings %>%
#   select(-z_count)

anova_compnov <- anova_test(compnov ~ Feedback * Two_or_three, 
                            data = combined_cig_ratings, 
                            type = 3)

anova_compnov

ANOVA Table (type III tests)

                 Effect DFn DFd     F     p p<.05      ges
1              Feedback   1  98 0.078 0.780       0.000798
2          Two_or_three   1  98 5.061 0.027     * 0.049000
3 Feedback:Two_or_three   1  98 6.984 0.010     * 0.067000

model_compnov <- lm(compnov ~ Feedback * Two_or_three, 
                    data = combined_cig_ratings)

emmeans(model_compnov, 
        pairwise ~ Feedback | Two_or_three)

$emmeans
Two_or_three = Cyclical:
 Feedback    emmean    SE df lower.CL upper.CL
 Feedback      2.96 0.196 98     2.57     3.35
 No Feedback   2.40 0.192 98     2.02     2.79

Two_or_three = Linear:
 Feedback    emmean    SE df lower.CL upper.CL
 Feedback      2.89 0.185 98     2.52     3.25
 No Feedback   3.34 0.189 98     2.96     3.71

Confidence level used: 0.95 

$contrasts
Two_or_three = Cyclical:
 contrast               estimate    SE df t.ratio p.value
 Feedback - No Feedback    0.557 0.275 98   2.027  0.0454

Two_or_three = Linear:
 contrast               estimate    SE df t.ratio p.value
 Feedback - No Feedback   -0.451 0.264 98  -1.705  0.0914

emmeans(model_compnov, 
        pairwise ~ Two_or_three | Feedback)

$emmeans
Feedback = Feedback:
 Two_or_three emmean    SE df lower.CL upper.CL
 Cyclical       2.96 0.196 98     2.57     3.35
 Linear         2.89 0.185 98     2.52     3.25

Feedback = No Feedback:
 Two_or_three emmean    SE df lower.CL upper.CL
 Cyclical       2.40 0.192 98     2.02     2.79
 Linear         3.34 0.189 98     2.96     3.71

Confidence level used: 0.95 

$contrasts
Feedback = Feedback:
 contrast          estimate   SE df t.ratio p.value
 Cyclical - Linear    0.075 0.27 98   0.278  0.7818

Feedback = No Feedback:
 contrast          estimate   SE df t.ratio p.value
 Cyclical - Linear   -0.933 0.27 98  -3.462  0.0008

# ggline(data = combined_cig_ratings, 
#        x= "Feedback", 
#        y = "compnov", 
#        color = "Two_or_three", 
#        add = "mean_se")

compnov_plot <- ggline(data = combined_cig_ratings, 
                          x= "Feedback", 
                          y = "compnov", 
                          color = "Two_or_three", 
                          add = "mean_ci",
                          palette = "jco",
                          ylab = "Complexity/Novelty",
                          size = 1.25,
                          legend = c(.924,.8),
                          legend.title = "",
                          ylim = c(1,5),
                          xlim = c(1.25,1.75),
                          position = position_dodge(width = .025))
compnov_plot

anova_zcount_thor <- anova_test(zcount_thor ~ Feedback * Two_or_three, 
                                data = combined_cig_ratings, 
                                type = 3)

anova_zcount_thor

ANOVA Table (type III tests)

                 Effect DFn DFd     F     p p<.05   ges
1              Feedback   1  98 1.563 0.214       0.016
2          Two_or_three   1  98 0.486 0.487       0.005
3 Feedback:Two_or_three   1  98 5.050 0.027     * 0.049

model_zcount_thor <- lm(zcount_thor ~ Feedback * Two_or_three, 
                        data = combined_cig_ratings)

emmeans(model_zcount_thor, 
        pairwise ~ Feedback | Two_or_three)

$emmeans
Two_or_three = Cyclical:
 Feedback     emmean    SE df lower.CL upper.CL
 Feedback     0.2522 0.184 98   -0.114   0.6180
 No Feedback -0.3740 0.181 98   -0.733  -0.0155

Two_or_three = Linear:
 Feedback     emmean    SE df lower.CL upper.CL
 Feedback    -0.0253 0.174 98   -0.370   0.3196
 No Feedback  0.1532 0.177 98   -0.198   0.5047

Confidence level used: 0.95 

$contrasts
Two_or_three = Cyclical:
 contrast               estimate    SE df t.ratio p.value
 Feedback - No Feedback    0.626 0.258 98   2.426  0.0171

Two_or_three = Linear:
 contrast               estimate    SE df t.ratio p.value
 Feedback - No Feedback   -0.178 0.248 98  -0.719  0.4737

emmeans(model_zcount_thor, 
        pairwise ~ Two_or_three | Feedback)

$emmeans
Feedback = Feedback:
 Two_or_three  emmean    SE df lower.CL upper.CL
 Cyclical      0.2522 0.184 98   -0.114   0.6180
 Linear       -0.0253 0.174 98   -0.370   0.3196

Feedback = No Feedback:
 Two_or_three  emmean    SE df lower.CL upper.CL
 Cyclical     -0.3740 0.181 98   -0.733  -0.0155
 Linear        0.1532 0.177 98   -0.198   0.5047

Confidence level used: 0.95 

$contrasts
Feedback = Feedback:
 contrast          estimate    SE df t.ratio p.value
 Cyclical - Linear    0.277 0.253 98   1.095  0.2762

Feedback = No Feedback:
 contrast          estimate    SE df t.ratio p.value
 Cyclical - Linear   -0.527 0.253 98  -2.084  0.0398

# ggline(data = combined_cig_ratings, 
#        x= "Feedback", 
#        y = "zcount_thor", 
#        color = "Two_or_three", 
#        add = "mean_se")

zcount_thor_plot <- ggline(data = combined_cig_ratings, 
                          x= "Feedback", 
                          y = "zcount_thor", 
                          color = "Two_or_three", 
                          add = "mean_ci",
                          palette = "jco",
                          ylab = "Word count/Thoroughness",
                          size = 1.25,
                          legend = c(.924,.8),
                          legend.title = "",
                          ylim = c(-1,1),
                          xlim = c(1.25,1.75),
                          position = position_dodge(width = .025))
zcount_thor_plot

# regressions with everything

reg1 <- lm(compnov ~ avg_nov_ideas + avg_nov_elabs + 
             total_ideas + total_elabs +
             Feedback + Two_or_three, 
           data = combined_cig_ratings)

summary(reg1)

Call:
lm(formula = compnov ~ avg_nov_ideas + avg_nov_elabs + total_ideas + 
    total_elabs + Feedback + Two_or_three, data = combined_cig_ratings)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.73143 -0.60938  0.08234  0.65974  1.67408 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)          0.311702   0.982378   0.317   0.7530  
avg_nov_ideas        0.558513   0.332261   1.681   0.1022  
avg_nov_elabs        0.178234   0.273473   0.652   0.5191  
total_ideas          0.002280   0.009204   0.248   0.8059  
total_elabs          0.024718   0.021992   1.124   0.2692  
FeedbackNo Feedback -0.019301   0.344158  -0.056   0.9556  
Two_or_threeLinear   0.725378   0.367974   1.971   0.0571 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.036 on 33 degrees of freedom
  (62 observations deleted due to missingness)
Multiple R-squared:  0.2575,    Adjusted R-squared:  0.1225 
F-statistic: 1.907 on 6 and 33 DF,  p-value: 0.109

lm.beta(reg1) #standardized Betas

Call:
lm(formula = compnov ~ avg_nov_ideas + avg_nov_elabs + total_ideas + 
    total_elabs + Feedback + Two_or_three, data = combined_cig_ratings)

Standardized Coefficients::
        (Intercept)       avg_nov_ideas       avg_nov_elabs         total_ideas 
                 NA         0.303930864         0.113448747         0.044128239 
        total_elabs FeedbackNo Feedback  Two_or_threeLinear 
        0.198627049        -0.008832958         0.331969499 

reg2 <- lm(zcount_thor ~ avg_nov_ideas + avg_nov_elabs + 
             total_ideas + total_elabs +
             Feedback + Two_or_three, 
           data = combined_cig_ratings)

summary(reg2)

Call:
lm(formula = zcount_thor ~ avg_nov_ideas + avg_nov_elabs + total_ideas + 
    total_elabs + Feedback + Two_or_three, data = combined_cig_ratings)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.55527 -0.59254  0.08691  0.55121  1.69877 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         -0.979494   0.859718  -1.139   0.2628  
avg_nov_ideas        0.449547   0.290775   1.546   0.1316  
avg_nov_elabs        0.089632   0.239327   0.375   0.7104  
total_ideas         -0.004726   0.008055  -0.587   0.5613  
total_elabs         -0.010872   0.019246  -0.565   0.5760  
FeedbackNo Feedback -0.602417   0.301187  -2.000   0.0538 .
Two_or_threeLinear   0.050888   0.322029   0.158   0.8754  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9071 on 33 degrees of freedom
  (62 observations deleted due to missingness)
Multiple R-squared:  0.2047,    Adjusted R-squared:  0.06005 
F-statistic: 1.415 on 6 and 33 DF,  p-value: 0.2383

lm.beta(reg2) #standardized Betas

Call:
lm(formula = zcount_thor ~ avg_nov_ideas + avg_nov_elabs + total_ideas + 
    total_elabs + Feedback + Two_or_three, data = combined_cig_ratings)

Standardized Coefficients::
        (Intercept)       avg_nov_ideas       avg_nov_elabs         total_ideas 
                 NA          0.28931263          0.06747222         -0.10818763 
        total_elabs FeedbackNo Feedback  Two_or_threeLinear 
        -0.10331982         -0.32604843          0.02754203