Load Data
## Warning: package 'ggplot2' was built under R version 4.3.1## Warning: package 'dplyr' was built under R version 4.3.1## ── Attaching core tidyverse packages 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tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────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tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsmulti_team_raw <- read.csv("1. In Paper/Team Ratings/t2_raw.csv")%>%
mutate(pid = row_number())%>%
mutate(
gender_text = case_when(
gender == 1 ~ "Man",
gender == 2 ~ "Woman",
gender == 3 ~ "NonBinary",
gender == 4 ~ "Other"),
t1_hiermut_6 = case_when(
t1_hiermut_6_r == 1 ~ 7,
t1_hiermut_6_r == 2 ~ 6,
t1_hiermut_6_r == 3 ~ 5,
t1_hiermut_6_r == 4 ~ 4,
t1_hiermut_6_r == 5 ~ 3,
t1_hiermut_6_r == 6 ~ 2,
t1_hiermut_6_r == 7 ~ 1),
t2_hiermut_6 = case_when(
t2_hiermut_6_r == 1 ~ 7,
t2_hiermut_6_r == 2 ~ 6,
t2_hiermut_6_r == 3 ~ 5,
t2_hiermut_6_r == 4 ~ 4,
t2_hiermut_6_r == 5 ~ 3,
t2_hiermut_6_r == 6 ~ 2,
t2_hiermut_6_r == 7 ~ 1),
t3_hiermut_6 = case_when(
t3_hiermut_6_r == 1 ~ 7,
t3_hiermut_6_r == 2 ~ 6,
t3_hiermut_6_r == 3 ~ 5,
t3_hiermut_6_r == 4 ~ 4,
t3_hiermut_6_r == 5 ~ 3,
t3_hiermut_6_r == 6 ~ 2,
t3_hiermut_6_r == 7 ~ 1),
t4_hiermut_6 = case_when(
t4_hiermut_6_r == 1 ~ 7,
t4_hiermut_6_r == 2 ~ 6,
t4_hiermut_6_r == 3 ~ 5,
t4_hiermut_6_r == 4 ~ 4,
t4_hiermut_6_r == 5 ~ 3,
t4_hiermut_6_r == 6 ~ 2,
t4_hiermut_6_r == 7 ~ 1),
t5_hiermut_6 = case_when(
t5_hiermut_6_r == 1 ~ 7,
t5_hiermut_6_r == 2 ~ 6,
t5_hiermut_6_r == 3 ~ 5,
t5_hiermut_6_r == 4 ~ 4,
t5_hiermut_6_r == 5 ~ 3,
t5_hiermut_6_r == 6 ~ 2,
t5_hiermut_6_r == 7 ~ 1),
t6_hiermut_6 = case_when(
t6_hiermut_6_r == 1 ~ 7,
t6_hiermut_6_r == 2 ~ 6,
t6_hiermut_6_r == 3 ~ 5,
t6_hiermut_6_r == 4 ~ 4,
t6_hiermut_6_r == 5 ~ 3,
t6_hiermut_6_r == 6 ~ 2,
t6_hiermut_6_r == 7 ~ 1),
t1_conf_3 = case_when(
t1_conf_3_r == 1 ~ 7,
t1_conf_3_r == 2 ~ 6,
t1_conf_3_r == 3 ~ 5,
t1_conf_3_r == 4 ~ 4,
t1_conf_3_r == 5 ~ 3,
t1_conf_3_r == 6 ~ 2,
t1_conf_3_r == 7 ~ 1),
t2_conf_3 = case_when(
t2_conf_3_r == 1 ~ 7,
t2_conf_3_r == 2 ~ 6,
t2_conf_3_r == 3 ~ 5,
t2_conf_3_r == 4 ~ 4,
t2_conf_3_r == 5 ~ 3,
t2_conf_3_r == 6 ~ 2,
t2_conf_3_r == 7 ~ 1),
t3_conf_3 = case_when(
t3_conf_3_r == 1 ~ 7,
t3_conf_3_r == 2 ~ 6,
t3_conf_3_r == 3 ~ 5,
t3_conf_3_r == 4 ~ 4,
t3_conf_3_r == 5 ~ 3,
t3_conf_3_r == 6 ~ 2,
t3_conf_3_r == 7 ~ 1),
t4_conf_3 = case_when(
t4_conf_3_r == 1 ~ 7,
t4_conf_3_r == 2 ~ 6,
t4_conf_3_r == 3 ~ 5,
t4_conf_3_r == 4 ~ 4,
t4_conf_3_r == 5 ~ 3,
t4_conf_3_r == 6 ~ 2,
t4_conf_3_r == 7 ~ 1),
t5_conf_3 = case_when(
t5_conf_3_r == 1 ~ 7,
t5_conf_3_r == 2 ~ 6,
t5_conf_3_r == 3 ~ 5,
t5_conf_3_r == 4 ~ 4,
t5_conf_3_r == 5 ~ 3,
t5_conf_3_r == 6 ~ 2,
t5_conf_3_r == 7 ~ 1),
t6_conf_3 = case_when(
t6_conf_3_r == 1 ~ 7,
t6_conf_3_r == 2 ~ 6,
t6_conf_3_r == 3 ~ 5,
t6_conf_3_r == 4 ~ 4,
t6_conf_3_r == 5 ~ 3,
t6_conf_3_r == 6 ~ 2,
t6_conf_3_r == 7 ~ 1))
multi_team_clean <- multi_team_raw %>%
rowwise() %>%
mutate(
t1_hiermut = mean(c(t1_hiermut_1, t1_hiermut_2, t1_hiermut_3, t1_hiermut_4, t1_hiermut_5, t1_hiermut_6)),
t1_futvoice = mean(c(t1_futvoice_1, t1_futvoice_2, t1_futvoice_3)),
t1_conf = mean(c(t1_conf_1, t1_conf_2, t1_conf_3)),
t1_voice = mean(c(t1_voice_1, t1_voice_2, t1_voice_3)),
t2_hiermut = mean(c(t2_hiermut_1, t2_hiermut_2, t2_hiermut_3, t2_hiermut_4, t2_hiermut_5, t2_hiermut_6)),
t2_futvoice = mean(c(t2_futvoice_1, t2_futvoice_2, t2_futvoice_3)),
t2_conf = mean(c(t2_conf_1, t2_conf_2, t2_conf_3)),
t2_voice = mean(c(t2_voice_1, t2_voice_2, t2_voice_3)),
t3_hiermut = mean(c(t3_hiermut_1, t3_hiermut_2, t3_hiermut_3, t3_hiermut_4, t3_hiermut_5, t3_hiermut_6)),
t3_futvoice = mean(c(t3_futvoice_1, t3_futvoice_2, t3_futvoice_3)),
t3_conf = mean(c(t3_conf_1, t3_conf_2, t3_conf_3)),
t3_voice = mean(c(t3_voice_1, t3_voice_2, t3_voice_3)),
t4_hiermut = mean(c(t4_hiermut_1, t4_hiermut_2, t4_hiermut_3, t4_hiermut_4, t4_hiermut_5, t4_hiermut_6)),
t4_futvoice = mean(c(t4_futvoice_1, t4_futvoice_2, t4_futvoice_3)),
t4_conf = mean(c(t4_conf_1, t4_conf_2, t4_conf_3)),
t4_voice = mean(c(t4_voice_1, t4_voice_2, t4_voice_3)),
t5_hiermut = mean(c(t5_hiermut_1, t5_hiermut_2, t5_hiermut_3, t5_hiermut_4, t5_hiermut_5, t5_hiermut_6)),
t5_futvoice = mean(c(t5_futvoice_1, t5_futvoice_2, t5_futvoice_3)),
t5_conf = mean(c(t5_conf_1, t5_conf_2, t5_conf_3)),
t5_voice = mean(c(t5_voice_1, t5_voice_2, t5_voice_3)),
t6_hiermut = mean(c(t6_hiermut_1, t6_hiermut_2, t6_hiermut_3, t6_hiermut_4, t6_hiermut_5, t6_hiermut_6)),
t6_futvoice = mean(c(t6_futvoice_1, t6_futvoice_2, t6_futvoice_3)),
t6_conf = mean(c(t6_conf_1, t6_conf_2, t6_conf_3)),
t6_voice = mean(c(t6_voice_1, t6_voice_2, t6_voice_3)),
t1_status_asc = mean(c(t1_status_ascrip_1, t1_status_ascrip_2, t1_status_ascrip_3, t1_status_ascrip_4, t1_status_ascrip_5)),
t2_status_asc = mean(c(t2_status_ascrip_1, t2_status_ascrip_2, t2_status_ascrip_3, t2_status_ascrip_4, t2_status_ascrip_5)),
t3_status_asc = mean(c(t3_status_ascrip_1, t3_status_ascrip_2, t3_status_ascrip_3, t3_status_ascrip_4, t3_status_ascrip_5)),
t4_status_asc = mean(c(t4_status_ascrip_1, t4_status_ascrip_2, t4_status_ascrip_3, t4_status_ascrip_4, t4_status_ascrip_5)),
t5_status_asc = mean(c(t5_status_ascrip_1, t5_status_ascrip_2, t5_status_ascrip_3, t5_status_ascrip_4, t5_status_ascrip_5)),
t6_status_asc = mean(c(t6_status_ascrip_1, t6_status_ascrip_2, t6_status_ascrip_3, t6_status_ascrip_4, t6_status_ascrip_5)),
pass = case_when(
attn_chk == 6 ~ 1,
attn_chk != 6 ~ 0))
multi_team_clean_wide <- multi_team_clean%>%
filter(attn_chk == 6)%>%
select(c(pid, t1_hiermut:t6_status_asc, gender_text, employment:attn_chk, pass)) %>%
pivot_longer(
cols = t1_hiermut:t6_status_asc,
names_to = "item",
values_to = "value"
)%>%
separate(item, into = c("team", "scale"), sep = "_")%>%
drop_na("value")%>%
select(-c(race_7_TEXT:pass))%>%
pivot_wider(
names_from = "scale")## Warning: Expected 2 pieces. Additional pieces discarded in 1920 rows [25, 26, 27, 28, 29, 30, 55, 56, 57, 58, 59, 60, 85, 86, 87, 88, 89, 90, 115, 116, ...].Participant Info
Gender info
##
## Man NonBinary Woman
## 0.54131054 0.01139601 0.44729345## [1] 38.39886## [1] 11.63838##
## Man NonBinary Woman
## 0.54131054 0.01139601 0.44729345## [1] 38.39886## [1] 11.63838## [1] "# that passed attn_chk: 320"Reliabilities
with(multi_team_clean, psych::alpha(data.frame(cbind(t1_hiermut_1, t1_hiermut_2, t1_hiermut_3, t1_hiermut_4, t1_hiermut_5, t1_hiermut_6))))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t1_hiermut_1, t1_hiermut_2,
## t1_hiermut_3, t1_hiermut_4, t1_hiermut_5, t1_hiermut_6)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.94 0.73 16 0.0051 4.1 1.6 0.76
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.94 0.95
## Duhachek 0.93 0.94 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t1_hiermut_1 0.93 0.93 0.92 0.72 13 0.0064 0.0146 0.77
## t1_hiermut_2 0.92 0.93 0.92 0.71 12 0.0066 0.0121 0.76
## t1_hiermut_3 0.93 0.93 0.92 0.72 13 0.0063 0.0140 0.78
## t1_hiermut_4 0.92 0.92 0.91 0.70 12 0.0069 0.0117 0.75
## t1_hiermut_5 0.92 0.92 0.92 0.71 12 0.0068 0.0141 0.75
## t1_hiermut_6 0.95 0.95 0.94 0.80 20 0.0041 0.0013 0.80
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t1_hiermut_1 348 0.89 0.89 0.87 0.84 4.2 1.8
## t1_hiermut_2 348 0.90 0.90 0.89 0.86 3.9 1.9
## t1_hiermut_3 348 0.88 0.88 0.86 0.83 4.4 1.8
## t1_hiermut_4 348 0.93 0.93 0.93 0.90 3.9 1.8
## t1_hiermut_5 348 0.92 0.92 0.91 0.88 4.0 1.9
## t1_hiermut_6 348 0.75 0.74 0.66 0.64 4.1 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t1_hiermut_1 0.09 0.11 0.14 0.20 0.19 0.16 0.11 0.01
## t1_hiermut_2 0.14 0.12 0.15 0.17 0.17 0.15 0.09 0.01
## t1_hiermut_3 0.08 0.11 0.11 0.21 0.17 0.18 0.14 0.01
## t1_hiermut_4 0.13 0.14 0.11 0.21 0.19 0.13 0.09 0.01
## t1_hiermut_5 0.14 0.13 0.13 0.19 0.15 0.17 0.09 0.01
## t1_hiermut_6 0.12 0.17 0.11 0.13 0.17 0.17 0.13 0.01with(multi_team_clean, psych::alpha(data.frame(cbind(t1_futvoice_1, t1_futvoice_2, t1_futvoice_3))))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t1_futvoice_1, t1_futvoice_2,
## t1_futvoice_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.91 0.83 15 0.0061 2.7 1.6 0.83
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.92 0.94 0.95
## Duhachek 0.92 0.94 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t1_futvoice_1 0.88 0.88 0.78 0.78 7.1 0.0132 NA 0.78
## t1_futvoice_2 0.91 0.91 0.83 0.83 9.9 0.0098 NA 0.83
## t1_futvoice_3 0.93 0.93 0.87 0.87 13.8 0.0072 NA 0.87
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t1_futvoice_1 348 0.96 0.96 0.94 0.90 2.6 1.7
## t1_futvoice_2 348 0.94 0.94 0.90 0.86 2.7 1.7
## t1_futvoice_3 348 0.93 0.93 0.86 0.83 2.7 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t1_futvoice_1 0.37 0.21 0.15 0.11 0.07 0.05 0.03 0.01
## t1_futvoice_2 0.35 0.22 0.15 0.12 0.08 0.05 0.04 0.01
## t1_futvoice_3 0.34 0.21 0.15 0.12 0.08 0.05 0.05 0.01##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t1_conf_1, t1_conf_2, t1_conf_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.6 0.62 0.66 0.35 1.6 0.039 4.9 1.3 0.15
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.53 0.6 0.67
## Duhachek 0.53 0.6 0.68
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t1_conf_1 0.26 0.26 0.15 0.15 0.35 0.079 NA 0.15
## t1_conf_2 0.22 0.22 0.12 0.12 0.28 0.083 NA 0.12
## t1_conf_3 0.88 0.88 0.79 0.79 7.41 0.013 NA 0.79
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t1_conf_1 348 0.83 0.84 0.83 0.57 4.7 1.7
## t1_conf_2 348 0.84 0.86 0.85 0.61 4.8 1.7
## t1_conf_3 348 0.59 0.56 0.16 0.14 5.1 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t1_conf_1 0.05 0.07 0.11 0.19 0.20 0.20 0.17 0.01
## t1_conf_2 0.04 0.06 0.09 0.19 0.21 0.22 0.18 0.01
## t1_conf_3 0.07 0.06 0.09 0.11 0.14 0.23 0.31 0.01##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t1_voice_1, t1_voice_2, t1_voice_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.89 0.79 12 0.0074 5.1 1.5 0.79
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.90 0.92 0.93
## Duhachek 0.91 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t1_voice_1 0.86 0.86 0.76 0.76 6.3 0.0146 NA 0.76
## t1_voice_2 0.91 0.91 0.83 0.83 9.9 0.0098 NA 0.83
## t1_voice_3 0.88 0.88 0.79 0.79 7.6 0.0124 NA 0.79
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t1_voice_1 348 0.94 0.94 0.90 0.86 5.2 1.6
## t1_voice_2 348 0.92 0.92 0.84 0.81 4.9 1.6
## t1_voice_3 348 0.93 0.93 0.88 0.84 5.2 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t1_voice_1 0.04 0.03 0.06 0.19 0.19 0.26 0.24 0.01
## t1_voice_2 0.05 0.04 0.07 0.22 0.20 0.23 0.20 0.01
## t1_voice_3 0.03 0.02 0.08 0.17 0.22 0.26 0.22 0.01with(multi_team_clean, psych::alpha(data.frame(cbind(t2_hiermut_1, t2_hiermut_2, t2_hiermut_3, t2_hiermut_4, t2_hiermut_5, t2_hiermut_6))))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t2_hiermut_1, t2_hiermut_2,
## t2_hiermut_3, t2_hiermut_4, t2_hiermut_5, t2_hiermut_6)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.94 0.93 0.71 14 0.0057 3.9 1.6 0.75
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.92 0.93 0.94
## Duhachek 0.92 0.93 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t2_hiermut_1 0.92 0.92 0.91 0.69 11 0.0074 0.0212 0.75
## t2_hiermut_2 0.91 0.92 0.91 0.68 11 0.0076 0.0179 0.75
## t2_hiermut_3 0.92 0.92 0.92 0.70 12 0.0071 0.0225 0.79
## t2_hiermut_4 0.91 0.91 0.91 0.68 11 0.0077 0.0188 0.75
## t2_hiermut_5 0.91 0.91 0.91 0.68 11 0.0078 0.0202 0.75
## t2_hiermut_6 0.95 0.95 0.94 0.80 20 0.0041 0.0015 0.80
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t2_hiermut_1 347 0.89 0.90 0.88 0.85 3.9 1.8
## t2_hiermut_2 347 0.91 0.91 0.90 0.87 3.9 1.8
## t2_hiermut_3 347 0.87 0.87 0.84 0.81 4.2 1.8
## t2_hiermut_4 347 0.91 0.92 0.91 0.87 3.8 1.8
## t2_hiermut_5 347 0.92 0.92 0.91 0.88 3.9 1.8
## t2_hiermut_6 347 0.71 0.70 0.59 0.58 4.0 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t2_hiermut_1 0.11 0.16 0.11 0.25 0.18 0.11 0.09 0.01
## t2_hiermut_2 0.14 0.15 0.11 0.21 0.18 0.12 0.09 0.01
## t2_hiermut_3 0.11 0.09 0.12 0.23 0.18 0.16 0.11 0.01
## t2_hiermut_4 0.14 0.14 0.14 0.22 0.18 0.09 0.09 0.01
## t2_hiermut_5 0.13 0.13 0.12 0.23 0.18 0.11 0.10 0.01
## t2_hiermut_6 0.13 0.18 0.13 0.14 0.14 0.14 0.14 0.01with(multi_team_clean, psych::alpha(data.frame(cbind(t2_futvoice_1, t2_futvoice_2, t2_futvoice_3))))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t2_futvoice_1, t2_futvoice_2,
## t2_futvoice_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.93 0.87 21 0.0042 2.8 1.8 0.88
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.95 0.95 0.96
## Duhachek 0.95 0.95 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t2_futvoice_1 0.92 0.92 0.85 0.85 12 0.0084 NA 0.85
## t2_futvoice_2 0.94 0.94 0.88 0.88 15 0.0067 NA 0.88
## t2_futvoice_3 0.94 0.94 0.89 0.89 16 0.0063 NA 0.89
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t2_futvoice_1 347 0.96 0.96 0.94 0.92 2.7 1.9
## t2_futvoice_2 347 0.96 0.96 0.92 0.90 2.8 1.9
## t2_futvoice_3 347 0.95 0.95 0.91 0.89 2.8 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t2_futvoice_1 0.41 0.2 0.10 0.09 0.07 0.08 0.05 0.01
## t2_futvoice_2 0.38 0.2 0.09 0.12 0.09 0.07 0.05 0.01
## t2_futvoice_3 0.35 0.2 0.13 0.10 0.07 0.09 0.05 0.01##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t2_conf_1, t2_conf_2, t2_conf_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.8 0.79 0.57 4 0.02 4.8 1.4 0.47
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.75 0.79 0.83
## Duhachek 0.75 0.79 0.83
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t2_conf_1 0.64 0.64 0.47 0.47 1.8 0.038 NA 0.47
## t2_conf_2 0.57 0.57 0.40 0.40 1.3 0.046 NA 0.40
## t2_conf_3 0.92 0.92 0.84 0.84 10.8 0.009 NA 0.84
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t2_conf_1 347 0.88 0.88 0.87 0.71 4.6 1.7
## t2_conf_2 347 0.91 0.91 0.91 0.78 4.8 1.6
## t2_conf_3 347 0.75 0.74 0.48 0.45 5.1 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t2_conf_1 0.06 0.07 0.12 0.20 0.21 0.17 0.16 0.01
## t2_conf_2 0.03 0.07 0.10 0.22 0.19 0.22 0.17 0.01
## t2_conf_3 0.03 0.08 0.11 0.12 0.14 0.22 0.30 0.01##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t2_voice_1, t2_voice_2, t2_voice_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.89 0.79 12 0.0074 4.9 1.5 0.8
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.90 0.92 0.93
## Duhachek 0.91 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t2_voice_1 0.89 0.89 0.80 0.80 8.1 0.012 NA 0.80
## t2_voice_2 0.89 0.89 0.80 0.80 8.0 0.012 NA 0.80
## t2_voice_3 0.87 0.87 0.78 0.78 7.0 0.013 NA 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t2_voice_1 347 0.93 0.93 0.87 0.83 4.9 1.7
## t2_voice_2 347 0.93 0.93 0.87 0.83 4.8 1.6
## t2_voice_3 347 0.93 0.93 0.89 0.85 5.0 1.6
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t2_voice_1 0.04 0.07 0.07 0.19 0.22 0.18 0.23 0.01
## t2_voice_2 0.04 0.05 0.10 0.20 0.24 0.18 0.19 0.01
## t2_voice_3 0.03 0.05 0.07 0.21 0.23 0.19 0.21 0.01with(multi_team_clean, psych::alpha(data.frame(cbind(t3_hiermut_1, t3_hiermut_2, t3_hiermut_3, t3_hiermut_4, t3_hiermut_5, t3_hiermut_6))))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t3_hiermut_1, t3_hiermut_2,
## t3_hiermut_3, t3_hiermut_4, t3_hiermut_5, t3_hiermut_6)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.94 0.72 16 0.0052 4 1.6 0.76
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.94 0.95
## Duhachek 0.93 0.94 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t3_hiermut_1 0.93 0.93 0.92 0.72 13 0.0065 0.0152 0.78
## t3_hiermut_2 0.92 0.93 0.92 0.71 13 0.0067 0.0141 0.78
## t3_hiermut_3 0.92 0.92 0.92 0.71 12 0.0068 0.0142 0.75
## t3_hiermut_4 0.92 0.92 0.91 0.70 11 0.0071 0.0121 0.75
## t3_hiermut_5 0.92 0.92 0.91 0.70 12 0.0070 0.0136 0.75
## t3_hiermut_6 0.95 0.95 0.94 0.80 20 0.0041 0.0014 0.80
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t3_hiermut_1 341 0.88 0.88 0.86 0.83 4.0 1.8
## t3_hiermut_2 341 0.89 0.89 0.87 0.84 3.9 1.9
## t3_hiermut_3 341 0.90 0.90 0.89 0.85 4.2 1.9
## t3_hiermut_4 341 0.93 0.93 0.93 0.89 3.9 1.8
## t3_hiermut_5 341 0.92 0.92 0.90 0.87 3.9 1.8
## t3_hiermut_6 341 0.75 0.74 0.64 0.63 3.9 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t3_hiermut_1 0.11 0.14 0.12 0.22 0.19 0.13 0.09 0.03
## t3_hiermut_2 0.13 0.14 0.15 0.18 0.15 0.16 0.09 0.03
## t3_hiermut_3 0.11 0.11 0.09 0.20 0.20 0.17 0.12 0.03
## t3_hiermut_4 0.13 0.12 0.14 0.23 0.19 0.12 0.07 0.03
## t3_hiermut_5 0.13 0.14 0.15 0.16 0.18 0.15 0.08 0.03
## t3_hiermut_6 0.16 0.15 0.14 0.11 0.14 0.20 0.10 0.03with(multi_team_clean, psych::alpha(data.frame(cbind(t3_futvoice_1, t3_futvoice_2, t3_futvoice_3))))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t3_futvoice_1, t3_futvoice_2,
## t3_futvoice_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.96 0.96 0.94 0.88 22 0.0041 2.8 1.9 0.88
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.95 0.96 0.96
## Duhachek 0.95 0.96 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t3_futvoice_1 0.92 0.92 0.85 0.85 11 0.0089 NA 0.85
## t3_futvoice_2 0.94 0.94 0.88 0.88 15 0.0068 NA 0.88
## t3_futvoice_3 0.95 0.95 0.91 0.91 21 0.0049 NA 0.91
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t3_futvoice_1 341 0.97 0.97 0.96 0.93 2.8 1.9
## t3_futvoice_2 341 0.96 0.96 0.93 0.91 2.9 2.0
## t3_futvoice_3 341 0.95 0.95 0.90 0.88 2.9 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t3_futvoice_1 0.35 0.21 0.14 0.10 0.07 0.06 0.07 0.03
## t3_futvoice_2 0.35 0.21 0.11 0.12 0.08 0.05 0.08 0.03
## t3_futvoice_3 0.33 0.21 0.13 0.12 0.06 0.06 0.09 0.03##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t3_conf_1, t3_conf_2, t3_conf_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.65 0.67 0.72 0.4 2 0.034 4.7 1.4 0.19
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.59 0.65 0.71
## Duhachek 0.59 0.65 0.72
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t3_conf_1 0.31 0.31 0.19 0.19 0.46 0.0730 NA 0.19
## t3_conf_2 0.30 0.30 0.17 0.17 0.42 0.0751 NA 0.17
## t3_conf_3 0.91 0.92 0.84 0.84 10.80 0.0091 NA 0.84
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t3_conf_1 341 0.86 0.87 0.88 0.63 4.4 1.8
## t3_conf_2 341 0.86 0.87 0.89 0.66 4.7 1.7
## t3_conf_3 341 0.61 0.58 0.20 0.19 5.1 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t3_conf_1 0.10 0.07 0.11 0.20 0.18 0.20 0.13 0.03
## t3_conf_2 0.06 0.07 0.09 0.21 0.20 0.22 0.15 0.03
## t3_conf_3 0.06 0.09 0.08 0.09 0.13 0.23 0.32 0.03##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t3_voice_1, t3_voice_2, t3_voice_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.91 0.84 16 0.0056 4.8 1.6 0.83
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.94 0.95
## Duhachek 0.93 0.94 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t3_voice_1 0.90 0.90 0.83 0.83 9.5 0.0102 NA 0.83
## t3_voice_2 0.93 0.93 0.86 0.86 12.4 0.0080 NA 0.86
## t3_voice_3 0.91 0.91 0.83 0.83 9.9 0.0098 NA 0.83
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t3_voice_1 341 0.95 0.95 0.92 0.89 4.8 1.7
## t3_voice_2 341 0.94 0.94 0.88 0.86 4.6 1.8
## t3_voice_3 341 0.95 0.95 0.91 0.88 4.9 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t3_voice_1 0.05 0.07 0.07 0.17 0.23 0.24 0.16 0.03
## t3_voice_2 0.07 0.06 0.12 0.19 0.20 0.18 0.18 0.03
## t3_voice_3 0.05 0.06 0.07 0.15 0.23 0.24 0.19 0.03with(multi_team_clean, psych::alpha(data.frame(cbind(t4_hiermut_1, t4_hiermut_2, t4_hiermut_3, t4_hiermut_4, t4_hiermut_5, t4_hiermut_6))))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t4_hiermut_1, t4_hiermut_2,
## t4_hiermut_3, t4_hiermut_4, t4_hiermut_5, t4_hiermut_6)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.94 0.94 0.94 0.72 16 0.0052 4 1.6 0.76
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.93 0.94 0.95
## Duhachek 0.93 0.94 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t4_hiermut_1 0.93 0.93 0.93 0.72 13 0.0066 0.022 0.75
## t4_hiermut_2 0.92 0.93 0.93 0.71 12 0.0067 0.022 0.76
## t4_hiermut_3 0.92 0.92 0.92 0.70 12 0.0068 0.015 0.76
## t4_hiermut_4 0.92 0.92 0.92 0.70 12 0.0069 0.014 0.75
## t4_hiermut_5 0.92 0.92 0.92 0.70 12 0.0070 0.015 0.76
## t4_hiermut_6 0.95 0.95 0.95 0.80 20 0.0040 0.003 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t4_hiermut_1 69 0.89 0.89 0.86 0.84 4.0 1.7
## t4_hiermut_2 69 0.90 0.90 0.87 0.85 3.7 1.8
## t4_hiermut_3 69 0.91 0.91 0.91 0.87 4.3 1.8
## t4_hiermut_4 69 0.92 0.92 0.91 0.87 3.8 1.8
## t4_hiermut_5 69 0.92 0.92 0.92 0.88 4.0 1.8
## t4_hiermut_6 69 0.73 0.73 0.64 0.61 3.9 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t4_hiermut_1 0.12 0.14 0.07 0.25 0.19 0.20 0.03 0.8
## t4_hiermut_2 0.10 0.25 0.12 0.12 0.23 0.16 0.03 0.8
## t4_hiermut_3 0.10 0.09 0.12 0.16 0.23 0.22 0.09 0.8
## t4_hiermut_4 0.12 0.17 0.12 0.19 0.20 0.16 0.04 0.8
## t4_hiermut_5 0.13 0.12 0.09 0.20 0.23 0.19 0.04 0.8
## t4_hiermut_6 0.09 0.22 0.14 0.13 0.16 0.19 0.07 0.8with(multi_team_clean, psych::alpha(data.frame(cbind(t4_futvoice_1, t4_futvoice_2, t4_futvoice_3))))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t4_futvoice_1, t4_futvoice_2,
## t4_futvoice_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.97 0.98 0.97 0.93 39 0.0024 3 2 0.93
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.97 0.97 0.98
## Duhachek 0.97 0.97 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t4_futvoice_1 0.95 0.95 0.91 0.91 20 0.0052 NA 0.91
## t4_futvoice_2 0.96 0.96 0.93 0.93 25 0.0042 NA 0.93
## t4_futvoice_3 0.97 0.98 0.95 0.95 39 0.0027 NA 0.95
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t4_futvoice_1 69 0.98 0.98 0.97 0.96 2.9 2.0
## t4_futvoice_2 69 0.98 0.98 0.96 0.95 2.9 1.9
## t4_futvoice_3 69 0.97 0.97 0.94 0.93 3.0 2.2
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t4_futvoice_1 0.38 0.14 0.14 0.13 0.03 0.10 0.07 0.8
## t4_futvoice_2 0.33 0.19 0.10 0.16 0.07 0.09 0.06 0.8
## t4_futvoice_3 0.38 0.13 0.14 0.09 0.04 0.12 0.10 0.8## Warning in psych::alpha(data.frame(cbind(t4_conf_1, t4_conf_2, t4_conf_3))): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( t4_conf_3 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t4_conf_1, t4_conf_2, t4_conf_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.44 0.47 0.66 0.23 0.89 0.057 4.6 1.3 -0.094
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.33 0.44 0.53
## Duhachek 0.32 0.44 0.55
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t4_conf_1 -0.20 -0.21 -0.094 -0.094 -0.17 0.1277 NA -0.094
## t4_conf_2 -0.21 -0.21 -0.095 -0.095 -0.17 0.1285 NA -0.095
## t4_conf_3 0.93 0.93 0.873 0.873 13.69 0.0073 NA 0.873
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t4_conf_1 69 0.83 0.85 0.90 0.534 4.5 1.8
## t4_conf_2 69 0.83 0.85 0.90 0.544 4.7 1.8
## t4_conf_3 69 0.43 0.39 -0.11 -0.098 4.5 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t4_conf_1 0.09 0.10 0.10 0.14 0.20 0.25 0.12 0.8
## t4_conf_2 0.10 0.03 0.10 0.13 0.22 0.29 0.13 0.8
## t4_conf_3 0.07 0.14 0.13 0.17 0.07 0.17 0.23 0.8##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t4_voice_1, t4_voice_2, t4_voice_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.92 0.88 0.79 11 0.0079 5 1.4 0.78
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.91 0.93
## Duhachek 0.9 0.91 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t4_voice_1 0.90 0.90 0.83 0.83 9.5 0.010 NA 0.83
## t4_voice_2 0.88 0.88 0.78 0.78 7.2 0.013 NA 0.78
## t4_voice_3 0.85 0.86 0.75 0.75 5.9 0.016 NA 0.75
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t4_voice_1 69 0.91 0.91 0.83 0.80 5.1 1.4
## t4_voice_2 69 0.93 0.93 0.87 0.83 4.8 1.6
## t4_voice_3 69 0.94 0.94 0.90 0.86 5.2 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t4_voice_1 0.00 0.03 0.09 0.28 0.17 0.22 0.22 0.8
## t4_voice_2 0.03 0.07 0.13 0.19 0.17 0.25 0.16 0.8
## t4_voice_3 0.03 0.01 0.04 0.19 0.30 0.23 0.19 0.8with(multi_team_clean, psych::alpha(data.frame(cbind(t5_hiermut_1, t5_hiermut_2, t5_hiermut_3, t5_hiermut_4, t5_hiermut_5, t5_hiermut_6))))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t5_hiermut_1, t5_hiermut_2,
## t5_hiermut_3, t5_hiermut_4, t5_hiermut_5, t5_hiermut_6)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.93 0.93 0.69 13 0.0059 3.8 1.7 0.68
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.92 0.93 0.94
## Duhachek 0.92 0.93 0.94
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t5_hiermut_1 0.92 0.92 0.91 0.69 10.9 0.0071 0.0128 0.67
## t5_hiermut_2 0.91 0.91 0.91 0.67 10.4 0.0074 0.0133 0.68
## t5_hiermut_3 0.90 0.90 0.89 0.65 9.2 0.0085 0.0090 0.66
## t5_hiermut_4 0.91 0.91 0.90 0.67 9.9 0.0079 0.0111 0.66
## t5_hiermut_5 0.92 0.92 0.92 0.69 11.1 0.0071 0.0134 0.71
## t5_hiermut_6 0.94 0.94 0.94 0.75 14.7 0.0055 0.0049 0.75
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t5_hiermut_1 28 0.85 0.86 0.83 0.79 3.6 1.8
## t5_hiermut_2 28 0.87 0.88 0.86 0.82 3.7 1.8
## t5_hiermut_3 28 0.93 0.93 0.94 0.90 4.3 2.0
## t5_hiermut_4 28 0.90 0.90 0.89 0.85 3.9 2.0
## t5_hiermut_5 28 0.85 0.85 0.81 0.78 4.1 2.0
## t5_hiermut_6 28 0.74 0.74 0.65 0.63 3.4 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t5_hiermut_1 0.18 0.14 0.14 0.14 0.25 0.14 0.00 0.92
## t5_hiermut_2 0.18 0.07 0.21 0.18 0.18 0.14 0.04 0.92
## t5_hiermut_3 0.18 0.07 0.04 0.18 0.14 0.32 0.07 0.92
## t5_hiermut_4 0.21 0.11 0.04 0.14 0.29 0.14 0.07 0.92
## t5_hiermut_5 0.18 0.07 0.14 0.07 0.25 0.18 0.11 0.92
## t5_hiermut_6 0.18 0.29 0.07 0.14 0.14 0.11 0.07 0.92with(multi_team_clean, psych::alpha(data.frame(cbind(t5_futvoice_1, t5_futvoice_2, t5_futvoice_3))))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t5_futvoice_1, t5_futvoice_2,
## t5_futvoice_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.87 0.76 9.5 0.009 3.1 1.7 0.74
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.9 0.92
## Duhachek 0.89 0.9 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t5_futvoice_1 0.82 0.82 0.69 0.69 4.5 0.0196 NA 0.69
## t5_futvoice_2 0.92 0.92 0.85 0.85 11.0 0.0089 NA 0.85
## t5_futvoice_3 0.85 0.85 0.74 0.74 5.8 0.0158 NA 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t5_futvoice_1 28 0.94 0.94 0.92 0.87 3.1 1.8
## t5_futvoice_2 28 0.88 0.88 0.78 0.74 3.1 1.8
## t5_futvoice_3 28 0.93 0.92 0.88 0.82 3.1 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t5_futvoice_1 0.29 0.11 0.25 0.11 0.07 0.18 0.00 0.92
## t5_futvoice_2 0.29 0.14 0.11 0.21 0.14 0.07 0.04 0.92
## t5_futvoice_3 0.32 0.11 0.14 0.21 0.04 0.11 0.07 0.92## Warning in psych::alpha(data.frame(cbind(t5_conf_1, t5_conf_2, t5_conf_3))): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( t5_conf_3 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t5_conf_1, t5_conf_2, t5_conf_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.34 0.48 0.61 0.24 0.92 0.067 4.7 1 -0.041
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.21 0.34 0.45
## Duhachek 0.21 0.34 0.47
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t5_conf_1 -0.075 -0.087 -0.041 -0.041 -0.08 0.100 NA -0.041
## t5_conf_2 -0.109 -0.115 -0.054 -0.054 -0.10 0.112 NA -0.054
## t5_conf_3 0.879 0.889 0.801 0.801 8.04 0.012 NA 0.801
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t5_conf_1 28 0.73 0.83 0.846 0.366 4.5 1.4
## t5_conf_2 28 0.72 0.84 0.853 0.448 5.0 1.1
## t5_conf_3 28 0.60 0.43 -0.056 -0.051 4.5 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t5_conf_1 0.04 0.04 0.21 0.14 0.29 0.29 0.00 0.92
## t5_conf_2 0.00 0.00 0.11 0.25 0.29 0.29 0.07 0.92
## t5_conf_3 0.07 0.07 0.21 0.14 0.11 0.18 0.21 0.92##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t5_voice_1, t5_voice_2, t5_voice_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.88 0.75 8.8 0.0097 5.3 1.1 0.73
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.9 0.92
## Duhachek 0.88 0.9 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t5_voice_1 0.78 0.78 0.64 0.64 3.6 0.0233 NA 0.64
## t5_voice_2 0.93 0.93 0.87 0.87 13.5 0.0074 NA 0.87
## t5_voice_3 0.84 0.84 0.73 0.73 5.3 0.0169 NA 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t5_voice_1 28 0.95 0.95 0.94 0.88 5.2 1.3
## t5_voice_2 28 0.86 0.87 0.74 0.71 5.4 1.2
## t5_voice_3 28 0.92 0.92 0.89 0.82 5.4 1.3
##
## Non missing response frequency for each item
## 2 3 4 5 6 7 miss
## t5_voice_1 0.00 0.11 0.21 0.25 0.25 0.18 0.92
## t5_voice_2 0.04 0.04 0.11 0.29 0.36 0.18 0.92
## t5_voice_3 0.00 0.07 0.18 0.25 0.25 0.25 0.92with(multi_team_clean, psych::alpha(data.frame(cbind(t6_hiermut_1, t6_hiermut_2, t6_hiermut_3, t6_hiermut_4, t6_hiermut_5, t6_hiermut_6))))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t6_hiermut_1, t6_hiermut_2,
## t6_hiermut_3, t6_hiermut_4, t6_hiermut_5, t6_hiermut_6)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.92 0.58 8.3 0.0098 4.1 1.5 0.58
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.87 0.89 0.9
## Duhachek 0.87 0.89 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t6_hiermut_1 0.83 0.84 0.87 0.51 5.3 0.0148 0.055 0.51
## t6_hiermut_2 0.85 0.86 0.90 0.56 6.3 0.0132 0.070 0.57
## t6_hiermut_3 0.90 0.91 0.92 0.66 9.8 0.0088 0.039 0.72
## t6_hiermut_4 0.84 0.85 0.89 0.52 5.5 0.0142 0.063 0.52
## t6_hiermut_5 0.84 0.85 0.89 0.53 5.6 0.0138 0.071 0.50
## t6_hiermut_6 0.91 0.92 0.93 0.69 11.2 0.0076 0.027 0.72
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t6_hiermut_1 16 0.94 0.94 0.96 0.91 4.2 1.8
## t6_hiermut_2 16 0.85 0.85 0.83 0.77 3.9 2.0
## t6_hiermut_3 16 0.64 0.64 0.55 0.48 4.7 2.0
## t6_hiermut_4 16 0.92 0.92 0.93 0.88 4.1 1.7
## t6_hiermut_5 16 0.90 0.91 0.89 0.86 3.7 1.7
## t6_hiermut_6 16 0.58 0.58 0.48 0.40 4.1 2.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t6_hiermut_1 0.06 0.19 0.06 0.25 0.12 0.25 0.06 0.95
## t6_hiermut_2 0.12 0.25 0.00 0.25 0.06 0.25 0.06 0.95
## t6_hiermut_3 0.12 0.06 0.00 0.19 0.25 0.19 0.19 0.95
## t6_hiermut_4 0.12 0.06 0.12 0.19 0.31 0.12 0.06 0.95
## t6_hiermut_5 0.06 0.31 0.06 0.19 0.19 0.19 0.00 0.95
## t6_hiermut_6 0.06 0.19 0.25 0.12 0.00 0.25 0.12 0.95with(multi_team_clean, psych::alpha(data.frame(cbind(t6_futvoice_1, t6_futvoice_2, t6_futvoice_3))))##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t6_futvoice_1, t6_futvoice_2,
## t6_futvoice_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.89 0.75 8.9 0.0096 3.5 1.9 0.66
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.9 0.92
## Duhachek 0.88 0.9 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t6_futvoice_1 0.78 0.79 0.65 0.65 3.7 0.0228 NA 0.65
## t6_futvoice_2 0.79 0.80 0.66 0.66 3.9 0.0219 NA 0.66
## t6_futvoice_3 0.96 0.96 0.93 0.93 27.5 0.0037 NA 0.93
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t6_futvoice_1 16 0.95 0.95 0.95 0.88 3.6 2.1
## t6_futvoice_2 16 0.95 0.94 0.95 0.88 3.6 2.1
## t6_futvoice_3 16 0.83 0.84 0.68 0.67 3.4 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t6_futvoice_1 0.25 0.12 0.06 0.19 0.12 0.19 0.06 0.95
## t6_futvoice_2 0.25 0.06 0.19 0.25 0.00 0.12 0.12 0.95
## t6_futvoice_3 0.25 0.12 0.12 0.19 0.12 0.19 0.00 0.95## Warning in psych::alpha(data.frame(cbind(t6_conf_1, t6_conf_2, t6_conf_3))): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( t6_conf_3 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t6_conf_1, t6_conf_2, t6_conf_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.42 0.47 0.55 0.23 0.89 0.057 4.3 1.3 0
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.31 0.42 0.52
## Duhachek 0.31 0.42 0.53
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t6_conf_1 0.000 0.000 0.000 0.000 0.000 0.103 NA 0.000
## t6_conf_2 -0.081 -0.081 -0.039 -0.039 -0.075 0.115 NA -0.039
## t6_conf_3 0.827 0.840 0.723 0.723 5.231 0.018 NA 0.723
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t6_conf_1 16 0.79 0.81 0.777 0.408 4.4 2.0
## t6_conf_2 16 0.78 0.82 0.802 0.511 4.2 1.6
## t6_conf_3 16 0.52 0.46 -0.023 -0.023 4.2 2.1
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t6_conf_1 0.19 0.00 0.12 0.12 0.12 0.38 0.06 0.95
## t6_conf_2 0.06 0.12 0.12 0.12 0.31 0.25 0.00 0.95
## t6_conf_3 0.12 0.06 0.25 0.12 0.12 0.06 0.25 0.95##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(t6_voice_1, t6_voice_2, t6_voice_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.75 0.81 0.51 3.1 0.023 4.8 1.3 0.47
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.71 0.76 0.8
## Duhachek 0.71 0.76 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## t6_voice_1 0.64 0.64 0.47 0.47 1.79 0.0382 NA 0.47
## t6_voice_2 0.91 0.91 0.84 0.84 10.18 0.0097 NA 0.84
## t6_voice_3 0.35 0.35 0.21 0.21 0.54 0.0695 NA 0.21
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## t6_voice_1 16 0.83 0.83 0.81 0.62 4.9 1.5
## t6_voice_2 16 0.69 0.69 0.46 0.36 4.9 1.6
## t6_voice_3 16 0.94 0.94 0.95 0.84 4.6 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## t6_voice_1 0.06 0.00 0.06 0.25 0.25 0.25 0.12 0.95
## t6_voice_2 0.00 0.06 0.19 0.12 0.19 0.25 0.19 0.95
## t6_voice_3 0.06 0.00 0.19 0.31 0.12 0.12 0.19 0.95Analyses
## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## step## Warning: package 'extraoperators' was built under R version 4.3.1## Warning: package 'JWileymisc' was built under R version 4.3.1Linear Models
## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: futvoice ~ hiermut + (1 + pid | team)
## Data: multi_team_clean_wide
##
## REML criterion at convergence: 4090.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6333 -0.7453 -0.2272 0.6553 2.9859
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## team (Intercept) 4.711e-02 0.217044
## pid 2.007e-06 0.001417 -1.00
## Residual 2.946e+00 1.716404
## Number of obs: 1041, groups: team, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.05177 0.13973 916.69071 29.00 <2e-16 ***
## hiermut -0.33423 0.03256 1033.68458 -10.27 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## hiermut -0.922
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 14.4157 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: conf ~ hiermut + (1 + pid | team)
## Data: multi_team_clean_wide
##
## REML criterion at convergence: 3524.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.01652 -0.66679 0.00745 0.71810 2.52018
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## team (Intercept) 1.471e-01 0.383507
## pid 1.177e-06 0.001085 -0.98
## Residual 1.702e+00 1.304539
## Number of obs: 1041, groups: team, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.508e+00 1.248e-01 7.999e+00 28.10 2.78e-09 ***
## hiermut 2.984e-01 2.479e-02 1.035e+03 12.04 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## hiermut -0.798
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 14.4157 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 1.00914 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: voice ~ hiermut + (1 + pid | team)
## Data: multi_team_clean_wide
##
## REML criterion at convergence: 3753.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.09542 -0.61864 0.09702 0.74069 2.12222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## team (Intercept) 1.206e-02 0.1097991
## pid 4.040e-07 0.0006356 -0.33
## Residual 2.129e+00 1.4590949
## Number of obs: 1041, groups: team, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.859e+00 1.322e-01 8.099e+00 29.20 1.7e-09 ***
## hiermut 2.815e-01 2.768e-02 1.033e+03 10.17 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## hiermut -0.823
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 1.00914 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?With Exploratory measures
## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -9.4e+00## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: futvoice ~ hiermut + status + gender + age + race + (1 + pid | team)
## Data: multi_team_clean_wide
##
## REML criterion at convergence: 3898.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3385 -0.6693 -0.1644 0.5286 3.7355
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## team (Intercept) 2.197e-01 0.468726
## pid 6.133e-06 0.002477 -1.00
## Residual 2.449e+00 1.565013
## Number of obs: 1041, groups: team, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.293e+00 2.831e-01 1.020e+03 18.697 < 2e-16 ***
## hiermut -1.432e-01 3.281e-02 1.022e+03 -4.364 1.4e-05 ***
## status -4.560e-01 3.202e-02 1.021e+03 -14.242 < 2e-16 ***
## gender -9.335e-02 9.719e-02 1.022e+03 -0.961 0.3370
## age 4.685e-03 4.613e-03 1.022e+03 1.016 0.3100
## race1,2 8.015e-02 3.520e-01 1.021e+03 0.228 0.8199
## race1,3 -5.175e-01 6.450e-01 1.021e+03 -0.802 0.4226
## race1,3,7 6.711e-01 9.125e-01 1.022e+03 0.735 0.4622
## race1,4 -6.915e-01 6.470e-01 1.021e+03 -1.069 0.2854
## race1,4,7 1.199e+00 6.444e-01 1.021e+03 1.861 0.0630 .
## race1,5 -8.082e-01 9.129e-01 1.021e+03 -0.885 0.3762
## race1,7 1.001e+00 9.276e-01 1.023e+03 1.079 0.2806
## race2 -7.792e-02 2.039e-01 1.019e+03 -0.382 0.7024
## race2,7 7.289e-01 9.101e-01 1.021e+03 0.801 0.4234
## race3 1.662e-02 1.709e-01 1.023e+03 0.097 0.9226
## race4 2.009e-01 1.533e-01 1.021e+03 1.311 0.1902
## race5 1.295e+00 6.456e-01 1.022e+03 2.006 0.0451 *
## race7 6.102e-01 3.759e-01 1.022e+03 1.623 0.1048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: conf ~ hiermut + status + gender + age + race + (1 + pid | team)
## Data: multi_team_clean_wide
##
## REML criterion at convergence: 2883
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8755 -0.6466 0.0630 0.6833 3.4039
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## team (Intercept) 1.662e-02 0.1289304
## pid 1.993e-07 0.0004465 -1.00
## Residual 9.125e-01 0.9552306
## Number of obs: 1041, groups: team, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.909e+00 1.734e-01 7.200e+02 11.014 <2e-16 ***
## hiermut 5.661e-02 1.998e-02 1.019e+03 2.833 0.0047 **
## status 5.794e-01 1.954e-02 1.012e+03 29.653 <2e-16 ***
## gender 1.093e-01 5.927e-02 1.022e+03 1.844 0.0655 .
## age -3.449e-03 2.810e-03 1.021e+03 -1.228 0.2199
## race1,2 9.533e-03 2.146e-01 1.023e+03 0.044 0.9646
## race1,3 6.508e-02 3.930e-01 1.014e+03 0.166 0.8685
## race1,3,7 -5.486e-01 5.563e-01 1.021e+03 -0.986 0.3243
## race1,4 1.545e-01 3.949e-01 1.021e+03 0.391 0.6957
## race1,4,7 2.867e-01 3.933e-01 1.021e+03 0.729 0.4662
## race1,5 8.830e-01 5.571e-01 1.022e+03 1.585 0.1133
## race1,7 -8.173e-01 5.650e-01 1.016e+03 -1.446 0.1484
## race2 9.063e-02 1.238e-01 9.831e+02 0.732 0.4642
## race2,7 -4.121e-01 5.555e-01 1.021e+03 -0.742 0.4584
## race3 -8.900e-04 1.042e-01 1.021e+03 -0.009 0.9932
## race4 -5.629e-02 9.354e-02 1.022e+03 -0.602 0.5474
## race5 5.749e-02 3.940e-01 1.021e+03 0.146 0.8840
## race7 1.638e-01 2.294e-01 1.022e+03 0.714 0.4752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 7.71557 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: voice ~ hiermut + status + gender + age + race + (pid | team)
## Data: multi_team_clean_wide
##
## REML criterion at convergence: 3301.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6970 -0.5422 0.0482 0.6208 3.7286
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## team (Intercept) 1.184e-01 0.3440796
## pid 6.503e-07 0.0008064 -0.96
## Residual 1.366e+00 1.1688233
## Number of obs: 1041, groups: team, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.147e+00 2.227e-01 8.079e+01 9.641 4.42e-15 ***
## hiermut 6.458e-02 2.447e-02 1.018e+03 2.640 0.008430 **
## status 5.268e-01 2.399e-02 1.022e+03 21.960 < 2e-16 ***
## gender 1.158e-02 7.255e-02 1.004e+03 0.160 0.873262
## age 6.475e-03 3.442e-03 1.019e+03 1.881 0.060224 .
## race1,2 2.299e-01 2.627e-01 1.012e+03 0.875 0.381719
## race1,3 1.788e+00 4.813e-01 1.015e+03 3.716 0.000214 ***
## race1,3,7 -1.726e+00 6.809e-01 1.015e+03 -2.534 0.011417 *
## race1,4 1.097e+00 4.833e-01 9.960e+02 2.271 0.023369 *
## race1,4,7 -3.189e-01 4.814e-01 9.949e+02 -0.662 0.507882
## race1,5 -3.953e-02 6.818e-01 1.001e+03 -0.058 0.953778
## race1,7 1.178e+00 6.918e-01 1.016e+03 1.703 0.088800 .
## race2 -2.133e-01 1.516e-01 9.724e+02 -1.407 0.159767
## race2,7 -7.229e-01 6.798e-01 9.945e+02 -1.063 0.287847
## race3 -2.227e-01 1.276e-01 1.014e+03 -1.745 0.081300 .
## race4 -2.376e-01 1.145e-01 1.001e+03 -2.075 0.038253 *
## race5 2.129e-01 4.822e-01 9.988e+02 0.442 0.658887
## race7 -2.769e-01 2.807e-01 1.001e+03 -0.987 0.324079
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 7.71557 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?With interactions
Status
summary(lmer(futvoice~hiermut*status+gender+age+race+(1+pid|team), data = multi_team_clean_wide)) # Significant## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+00## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: futvoice ~ hiermut * status + gender + age + race + (1 + pid | team)
## Data: multi_team_clean_wide
##
## REML criterion at convergence: 3888.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7496 -0.6370 -0.2211 0.5528 3.5551
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## team (Intercept) 2.123e-01 0.460779
## pid 6.132e-06 0.002476 -1.00
## Residual 2.413e+00 1.553330
## Number of obs: 1041, groups: team, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.397e+00 3.920e-01 1.020e+03 16.320 < 2e-16 ***
## hiermut -4.648e-01 8.593e-02 1.021e+03 -5.409 7.88e-08 ***
## status -6.991e-01 6.800e-02 1.022e+03 -10.281 < 2e-16 ***
## gender -7.791e-02 9.654e-02 1.021e+03 -0.807 0.4199
## age 3.649e-03 4.585e-03 1.021e+03 0.796 0.4264
## race1,2 6.159e-02 3.494e-01 1.020e+03 0.176 0.8601
## race1,3 -6.559e-01 6.411e-01 1.020e+03 -1.023 0.3065
## race1,3,7 5.324e-01 9.063e-01 1.021e+03 0.587 0.5571
## race1,4 -6.501e-01 6.422e-01 1.021e+03 -1.012 0.3117
## race1,4,7 1.259e+00 6.398e-01 1.021e+03 1.968 0.0493 *
## race1,5 -8.289e-01 9.061e-01 1.020e+03 -0.915 0.3605
## race1,7 1.144e+00 9.214e-01 1.022e+03 1.242 0.2145
## race2 -5.851e-02 2.024e-01 1.019e+03 -0.289 0.7726
## race2,7 6.582e-01 9.035e-01 1.021e+03 0.729 0.4665
## race3 -7.512e-03 1.698e-01 1.022e+03 -0.044 0.9647
## race4 2.271e-01 1.522e-01 1.020e+03 1.492 0.1361
## race5 1.343e+00 6.409e-01 1.021e+03 2.095 0.0364 *
## race7 5.088e-01 3.739e-01 1.021e+03 1.361 0.1739
## hiermut:status 6.750e-02 1.669e-02 1.022e+03 4.044 5.65e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 19 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+01## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: conf ~ hiermut * status + gender + age + race + (1 + pid | team)
## Data: multi_team_clean_wide
##
## REML criterion at convergence: 2888.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9019 -0.6355 0.0674 0.6935 3.4552
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## team (Intercept) 7.753e-02 0.2784408
## pid 8.462e-07 0.0009199 -1.00
## Residual 9.079e-01 0.9528194
## Number of obs: 1041, groups: team, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.534e+00 2.413e-01 7.683e+02 6.355 3.58e-10 ***
## hiermut 1.641e-01 5.269e-02 1.021e+03 3.114 0.0019 **
## status 6.602e-01 4.172e-02 1.021e+03 15.825 < 2e-16 ***
## gender 1.045e-01 5.919e-02 1.021e+03 1.766 0.0777 .
## age -3.156e-03 2.811e-03 1.020e+03 -1.123 0.2618
## race1,2 1.556e-02 2.143e-01 1.021e+03 0.073 0.9421
## race1,3 1.022e-01 3.931e-01 1.019e+03 0.260 0.7949
## race1,3,7 -4.947e-01 5.556e-01 1.021e+03 -0.890 0.3735
## race1,4 1.386e-01 3.940e-01 1.022e+03 0.352 0.7251
## race1,4,7 2.652e-01 3.925e-01 1.022e+03 0.676 0.4994
## race1,5 8.845e-01 5.558e-01 1.022e+03 1.591 0.1118
## race1,7 -8.554e-01 5.646e-01 1.018e+03 -1.515 0.1301
## race2 8.085e-02 1.237e-01 1.003e+03 0.653 0.5137
## race2,7 -3.908e-01 5.542e-01 1.022e+03 -0.705 0.4809
## race3 8.631e-03 1.041e-01 1.022e+03 0.083 0.9339
## race4 -6.516e-02 9.341e-02 1.022e+03 -0.698 0.4856
## race5 4.325e-02 3.931e-01 1.022e+03 0.110 0.9124
## race7 1.976e-01 2.293e-01 1.022e+03 0.861 0.3892
## hiermut:status -2.242e-02 1.023e-02 1.022e+03 -2.191 0.0287 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 19 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 6.59812 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: voice ~ hiermut * status + gender + age + race + (pid | team)
## Data: multi_team_clean_wide
##
## REML criterion at convergence: 3308.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6940 -0.5406 0.0480 0.6227 3.7259
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## team (Intercept) 1.177e-01 0.3431067
## pid 7.456e-07 0.0008635 -0.96
## Residual 1.367e+00 1.1693658
## Number of obs: 1041, groups: team, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.132e+00 3.022e-01 2.799e+02 7.055 1.36e-11 ***
## hiermut 6.753e-02 6.467e-02 1.014e+03 1.044 0.296636
## status 5.289e-01 5.127e-02 1.020e+03 10.317 < 2e-16 ***
## gender 1.167e-02 7.264e-02 1.002e+03 0.161 0.872425
## age 6.494e-03 3.449e-03 1.019e+03 1.883 0.060036 .
## race1,2 2.299e-01 2.629e-01 1.011e+03 0.874 0.382072
## race1,3 1.790e+00 4.823e-01 1.015e+03 3.712 0.000217 ***
## race1,3,7 -1.726e+00 6.818e-01 1.012e+03 -2.532 0.011503 *
## race1,4 1.097e+00 4.835e-01 9.942e+02 2.268 0.023525 *
## race1,4,7 -3.199e-01 4.817e-01 9.932e+02 -0.664 0.506823
## race1,5 -3.942e-02 6.821e-01 9.991e+02 -0.058 0.953927
## race1,7 1.175e+00 6.927e-01 1.016e+03 1.696 0.090139 .
## race2 -2.123e-01 1.518e-01 9.859e+02 -1.399 0.162251
## race2,7 -7.228e-01 6.802e-01 9.929e+02 -1.063 0.288233
## race3 -2.232e-01 1.278e-01 1.011e+03 -1.747 0.080962 .
## race4 -2.376e-01 1.146e-01 9.989e+02 -2.073 0.038436 *
## race5 2.119e-01 4.825e-01 9.968e+02 0.439 0.660639
## race7 -2.763e-01 2.815e-01 9.994e+02 -0.982 0.326470
## hiermut:status -5.958e-04 1.256e-02 1.010e+03 -0.047 0.962184
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 19 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 6.59812 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?sjPlot::plot_model(lmer(futvoice~hiermut*status+gender+age+race+(1+pid|team), data = multi_team_clean_wide),
type = "int",
mdrt.values = "meansd")## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+00
#### Gender
## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -9.3e+00## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: futvoice ~ hiermut * gender + status + age + race + (1 + pid | team)
## Data: multi_team_clean_wide
##
## REML criterion at convergence: 3902.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3457 -0.6681 -0.1627 0.5269 3.7407
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## team (Intercept) 2.106e-01 0.458941
## pid 5.981e-06 0.002446 -1.00
## Residual 2.452e+00 1.565756
## Number of obs: 1041, groups: team, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.357e+00 4.391e-01 1.022e+03 12.199 <2e-16 ***
## hiermut -1.596e-01 9.274e-02 1.020e+03 -1.721 0.0855 .
## gender -1.377e-01 2.526e-01 1.021e+03 -0.545 0.5859
## status -4.560e-01 3.203e-02 1.020e+03 -14.235 <2e-16 ***
## age 4.726e-03 4.621e-03 1.021e+03 1.023 0.3066
## race1,2 7.661e-02 3.525e-01 1.020e+03 0.217 0.8280
## race1,3 -5.209e-01 6.456e-01 1.020e+03 -0.807 0.4200
## race1,3,7 6.774e-01 9.134e-01 1.021e+03 0.742 0.4585
## race1,4 -6.909e-01 6.473e-01 1.020e+03 -1.067 0.2860
## race1,4,7 1.196e+00 6.450e-01 1.021e+03 1.855 0.0639 .
## race1,5 -8.029e-01 9.137e-01 1.020e+03 -0.879 0.3797
## race1,7 1.026e+00 9.366e-01 1.022e+03 1.095 0.2736
## race2 -8.046e-02 2.044e-01 1.019e+03 -0.394 0.6939
## race2,7 7.264e-01 9.107e-01 1.021e+03 0.798 0.4252
## race3 1.499e-02 1.713e-01 1.022e+03 0.087 0.9303
## race4 2.011e-01 1.533e-01 1.020e+03 1.312 0.1899
## race5 1.291e+00 6.463e-01 1.021e+03 1.998 0.0459 *
## race7 6.136e-01 3.765e-01 1.021e+03 1.630 0.1034
## hiermut:gender 1.129e-02 5.951e-02 1.021e+03 0.190 0.8495
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 19 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+01## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: conf ~ hiermut * gender + status + age + race + (1 + pid | team)
## Data: multi_team_clean_wide
##
## REML criterion at convergence: 2890.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8779 -0.6308 0.0670 0.6845 3.4346
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## team (Intercept) 7.563e-02 0.2750003
## pid 9.336e-07 0.0009662 -1.00
## Residual 9.119e-01 0.9549205
## Number of obs: 1041, groups: team, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.778e+00 2.683e-01 9.381e+02 6.627 5.77e-11 ***
## hiermut 8.955e-02 5.655e-02 1.021e+03 1.584 0.114
## gender 1.965e-01 1.540e-01 1.022e+03 1.275 0.202
## status 5.794e-01 1.956e-02 1.014e+03 29.614 < 2e-16 ***
## age -3.593e-03 2.817e-03 1.021e+03 -1.276 0.202
## race1,2 1.447e-02 2.150e-01 1.021e+03 0.067 0.946
## race1,3 6.348e-02 3.936e-01 1.021e+03 0.161 0.872
## race1,3,7 -5.525e-01 5.568e-01 1.021e+03 -0.992 0.321
## race1,4 1.516e-01 3.948e-01 1.022e+03 0.384 0.701
## race1,4,7 2.917e-01 3.934e-01 1.022e+03 0.742 0.459
## race1,5 8.682e-01 5.573e-01 1.022e+03 1.558 0.120
## race1,7 -8.550e-01 5.707e-01 1.019e+03 -1.498 0.134
## race2 9.265e-02 1.243e-01 1.004e+03 0.746 0.456
## race2,7 -4.088e-01 5.554e-01 1.022e+03 -0.736 0.462
## race3 3.889e-03 1.044e-01 1.022e+03 0.037 0.970
## race4 -5.638e-02 9.353e-02 1.022e+03 -0.603 0.547
## race5 6.663e-02 3.941e-01 1.022e+03 0.169 0.866
## race7 1.578e-01 2.296e-01 1.022e+03 0.688 0.492
## hiermut:gender -2.216e-02 3.629e-02 1.022e+03 -0.611 0.542
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 19 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## boundary (singular) fit: see help('isSingular')## Warning: Model failed to converge with 1 negative eigenvalue: -1.0e+07## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: voice ~ hiermut * gender + status + gender + age + race + (pid | team)
## Data: multi_team_clean_wide
##
## REML criterion at convergence: 3306.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6893 -0.5390 0.0480 0.6209 3.7391
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## team (Intercept) 1.185e-01 0.344282
## pid 5.227e-07 0.000723 -0.99
## Residual 1.368e+00 1.169721
## Number of obs: 1041, groups: team, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.114e+00 3.340e-01 3.219e+02 6.330 8.23e-10 ***
## hiermut 7.362e-02 6.926e-02 1.014e+03 1.063 0.288017
## gender 3.664e-02 1.887e-01 1.014e+03 0.194 0.846063
## status 5.272e-01 2.400e-02 1.022e+03 21.962 < 2e-16 ***
## age 6.434e-03 3.449e-03 1.016e+03 1.865 0.062410 .
## race1,2 2.319e-01 2.632e-01 1.017e+03 0.881 0.378528
## race1,3 1.790e+00 4.817e-01 1.009e+03 3.715 0.000214 ***
## race1,3,7 -1.727e+00 6.816e-01 1.016e+03 -2.533 0.011455 *
## race1,4 1.097e+00 4.836e-01 1.012e+03 2.269 0.023462 *
## race1,4,7 -3.162e-01 4.819e-01 1.012e+03 -0.656 0.511854
## race1,5 -4.237e-02 6.826e-01 1.014e+03 -0.062 0.950515
## race1,7 1.166e+00 6.984e-01 1.011e+03 1.670 0.095232 .
## race2 -2.130e-01 1.519e-01 9.894e+02 -1.402 0.161108
## race2,7 -7.211e-01 6.804e-01 1.011e+03 -1.060 0.289474
## race3 -2.207e-01 1.279e-01 1.017e+03 -1.726 0.084641 .
## race4 -2.377e-01 1.146e-01 1.014e+03 -2.074 0.038316 *
## race5 2.158e-01 4.828e-01 1.013e+03 0.447 0.655047
## race7 -2.786e-01 2.812e-01 1.013e+03 -0.991 0.322012
## hiermut:gender -6.442e-03 4.445e-02 1.014e+03 -0.145 0.884803
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 19 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')