if (!require(haven)){
install.packages("haven", dependencies = TRUE)
require(haven)
}Loading required package: havenData Analysis
Load Packages
if (!require(tidyverse)){
install.packages("tidyverse", dependencies = TRUE)
require(tidyverse)
}Loading required package: tidyverse── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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install.packages("psych", dependencies = TRUE)
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Attaching package: 'psych'The following objects are masked from 'package:ggplot2':
%+%, alphaif (!require(afex)){
install.packages("afex", dependencies = TRUE)
require(afex)
}Loading required package: afexLoading required package: lme4Loading required package: Matrix
Attaching package: 'Matrix'The following objects are masked from 'package:tidyr':
expand, pack, unpack************
Welcome to afex. For support visit: http://afex.singmann.science/- Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
- Methods for calculating p-values with mixed(): 'S', 'KR', 'LRT', and 'PB'
- 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
- Get and set global package options with: afex_options()
- Set sum-to-zero contrasts globally: set_sum_contrasts()
- For example analyses see: browseVignettes("afex")
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Attaching package: 'afex'The following object is masked from 'package:lme4':
lmerImport Data
SPSS
dataset <- read_sav("CincoData.sav")Clean Data
(dataset %>%
filter(Duration__in_seconds_ > 120) %>%
filter(!is.na(Stress1)) %>%
mutate(age_as_a_number = as.numeric(Age)) -> dataset.clean)# A tibble: 34 × 31
StartDate EndDate Status IPAddress Progress
<dttm> <dttm> <dbl+lbl> <chr> <dbl>
1 2024-04-03 11:20:26 2024-04-03 11:23:06 0 [IP Address] 67.190.227.16 100
2 2024-04-03 11:21:29 2024-04-03 11:23:35 0 [IP Address] 139.62.223.1… 100
3 2024-04-03 11:34:39 2024-04-03 11:40:53 0 [IP Address] 73.53.224.92 100
4 2024-04-03 11:43:20 2024-04-03 11:46:09 0 [IP Address] 174.212.33.2… 100
5 2024-04-03 11:55:18 2024-04-03 11:57:59 0 [IP Address] 99.171.78.207 100
6 2024-04-03 12:27:56 2024-04-03 12:32:24 0 [IP Address] 104.28.92.177 100
7 2024-04-03 12:33:26 2024-04-03 12:35:30 0 [IP Address] 172.59.69.207 100
8 2024-04-03 13:02:23 2024-04-03 13:10:47 0 [IP Address] 38.85.147.114 100
9 2024-04-03 13:18:30 2024-04-03 13:21:08 0 [IP Address] 50.204.243.1… 100
10 2024-04-03 13:27:04 2024-04-03 13:29:20 0 [IP Address] 172.59.69.78 100
# ℹ 24 more rows
# ℹ 26 more variables: Duration__in_seconds_ <dbl>, Finished <dbl+lbl>,
# RecordedDate <dttm>, ResponseId <chr>, RecipientLastName <chr>,
# RecipientFirstName <chr>, RecipientEmail <chr>, ExternalReference <chr>,
# LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
# UserLanguage <chr>, Q1_3 <dbl+lbl>, Q1_4 <dbl+lbl>, Stress1 <dbl+lbl>,
# Gender <chr>, Race <chr>, Age <chr>, Exit_question <chr>, …Create Compensation IV
dataset.clean %>%
mutate(compensationIV = case_when(FL_10_DO_story1AboveavgCom_CollegedegreeAshley == 1 ~ "Above",
FL_10_DO_Story2AboveAvgComp_NoCollDegMakenzie == 1 ~ "Above",
FL_10_DO_Story3BelowAvgComp_CollegeDegMillicent == 1 ~ "Below",
FL_10_DO_Story4BelowAvg_NoCollDegAraya == 1 ~ "Below" )) -> dataset.cleanCreate Degree IV
dataset.clean %>%
mutate(degreeIV = case_when(FL_10_DO_story1AboveavgCom_CollegedegreeAshley == 1 ~ "Yes",
FL_10_DO_Story2AboveAvgComp_NoCollDegMakenzie == 1 ~ "No",
FL_10_DO_Story3BelowAvgComp_CollegeDegMillicent == 1 ~ "Yes",
FL_10_DO_Story4BelowAvg_NoCollDegAraya == 1 ~ "No" )) -> dataset.cleanANOVA
dataset.clean %>%
mutate(DV = as.numeric(Stress1)) -> dataset.cleanaov_ez(id = "ResponseId", dv = "DV", data = dataset.clean, between=c("degreeIV", "compensationIV"))Converting to factor: degreeIV, compensationIVContrasts set to contr.sum for the following variables: degreeIV, compensationIVAnova Table (Type 3 tests)
Response: DV
Effect df MSE F ges p.value
1 degreeIV 1, 30 1.59 0.02 <.001 .891
2 compensationIV 1, 30 1.59 3.62 + .108 .067
3 degreeIV:compensationIV 1, 30 1.59 1.56 .049 .222
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Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1