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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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%+%, alphaImport Data
dataset <- read_sav("Psykicks Mindfulness Data.sav")Cleaning based on progress
(dataset %>%
filter(Progress > 79) -> dataset.clean)# A tibble: 124 × 44
StartDate EndDate Status IPAddress Progress
<dttm> <dttm> <dbl+lbl> <chr> <dbl>
1 2024-03-13 16:47:14 2024-03-13 16:47:23 0 [IP Address] 73.224.66.184 100
2 2024-03-13 19:17:10 2024-03-13 19:21:25 0 [IP Address] 76.122.42.81 100
3 2024-03-13 19:34:32 2024-03-13 19:45:01 0 [IP Address] 73.6.5.118 100
4 2024-03-13 21:12:43 2024-03-13 21:26:04 0 [IP Address] 92.119.18.122 100
5 2024-03-14 09:56:42 2024-03-14 10:03:37 0 [IP Address] 139.62.222.1… 100
6 2024-03-15 01:31:54 2024-03-15 15:07:03 0 [IP Address] 107.115.224.… 100
7 2024-03-14 11:15:41 2024-03-14 11:24:47 0 [IP Address] 65.87.105.57 97
8 2024-03-14 13:06:19 2024-03-14 13:25:21 0 [IP Address] 166.205.159.… 97
9 2024-03-22 12:31:08 2024-03-22 12:40:46 0 [IP Address] 104.28.32.246 100
10 2024-03-25 17:07:22 2024-03-25 17:15:07 0 [IP Address] 139.62.222.2… 100
# ℹ 114 more rows
# ℹ 39 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>, Informed_Consent <dbl+lbl>, TP_1 <dbl+lbl>,
# TP_3 <dbl+lbl>, TP_4 <dbl+lbl>, TP_5 <dbl+lbl>, TP_2 <dbl+lbl>, …Cleaning based on duration in seconds
(dataset.clean %>%
filter(Duration__in_seconds_ > 120) -> dataset.clean)# A tibble: 119 × 44
StartDate EndDate Status IPAddress Progress
<dttm> <dttm> <dbl+lbl> <chr> <dbl>
1 2024-03-13 19:17:10 2024-03-13 19:21:25 0 [IP Address] 76.122.42.81 100
2 2024-03-13 19:34:32 2024-03-13 19:45:01 0 [IP Address] 73.6.5.118 100
3 2024-03-13 21:12:43 2024-03-13 21:26:04 0 [IP Address] 92.119.18.122 100
4 2024-03-14 09:56:42 2024-03-14 10:03:37 0 [IP Address] 139.62.222.1… 100
5 2024-03-15 01:31:54 2024-03-15 15:07:03 0 [IP Address] 107.115.224.… 100
6 2024-03-14 11:15:41 2024-03-14 11:24:47 0 [IP Address] 65.87.105.57 97
7 2024-03-14 13:06:19 2024-03-14 13:25:21 0 [IP Address] 166.205.159.… 97
8 2024-03-22 12:31:08 2024-03-22 12:40:46 0 [IP Address] 104.28.32.246 100
9 2024-03-25 17:07:22 2024-03-25 17:15:07 0 [IP Address] 139.62.222.2… 100
10 2024-03-25 17:53:57 2024-03-25 18:05:40 0 [IP Address] 172.59.67.194 100
# ℹ 109 more rows
# ℹ 39 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>, Informed_Consent <dbl+lbl>, TP_1 <dbl+lbl>,
# TP_3 <dbl+lbl>, TP_4 <dbl+lbl>, TP_5 <dbl+lbl>, TP_2 <dbl+lbl>, …Create Mindfulness Meditation IV
(dataset.clean %>%
mutate(MindfulnessIV = case_when(FL_10_DO_NoMindfulnessMeditation_PhysiologicalArousal == 1 ~ "No Mindfulness",
FL_10_DO_NoMindfulnessMeditation_NoPhysiologicalArousal == 1 ~ "No Mindfulness",
FL_10_DO_MindfulnessMeditation_PhysiologicalArousal == 1 ~ "Mindfulness",
FL_10_DO_MindfulnessMeditation_NoPhysiologicalArousal == 1 ~ "Mindfulness")) -> dataset.clean)# A tibble: 119 × 45
StartDate EndDate Status IPAddress Progress
<dttm> <dttm> <dbl+lbl> <chr> <dbl>
1 2024-03-13 19:17:10 2024-03-13 19:21:25 0 [IP Address] 76.122.42.81 100
2 2024-03-13 19:34:32 2024-03-13 19:45:01 0 [IP Address] 73.6.5.118 100
3 2024-03-13 21:12:43 2024-03-13 21:26:04 0 [IP Address] 92.119.18.122 100
4 2024-03-14 09:56:42 2024-03-14 10:03:37 0 [IP Address] 139.62.222.1… 100
5 2024-03-15 01:31:54 2024-03-15 15:07:03 0 [IP Address] 107.115.224.… 100
6 2024-03-14 11:15:41 2024-03-14 11:24:47 0 [IP Address] 65.87.105.57 97
7 2024-03-14 13:06:19 2024-03-14 13:25:21 0 [IP Address] 166.205.159.… 97
8 2024-03-22 12:31:08 2024-03-22 12:40:46 0 [IP Address] 104.28.32.246 100
9 2024-03-25 17:07:22 2024-03-25 17:15:07 0 [IP Address] 139.62.222.2… 100
10 2024-03-25 17:53:57 2024-03-25 18:05:40 0 [IP Address] 172.59.67.194 100
# ℹ 109 more rows
# ℹ 40 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>, Informed_Consent <dbl+lbl>, TP_1 <dbl+lbl>,
# TP_3 <dbl+lbl>, TP_4 <dbl+lbl>, TP_5 <dbl+lbl>, TP_2 <dbl+lbl>, …Create Physiological Arousal IV
(dataset.clean %>%
mutate(PhysiologicalArousalIV = case_when(FL_10_DO_NoMindfulnessMeditation_PhysiologicalArousal == 1 ~ "Physiological Arousal",
FL_10_DO_NoMindfulnessMeditation_NoPhysiologicalArousal == 1 ~ "No Physiological Arousal",
FL_10_DO_MindfulnessMeditation_PhysiologicalArousal == 1 ~ "Physiological Arousal",
FL_10_DO_MindfulnessMeditation_NoPhysiologicalArousal == 1 ~ "No Physiological Arousal")) -> dataset.clean)# A tibble: 119 × 46
StartDate EndDate Status IPAddress Progress
<dttm> <dttm> <dbl+lbl> <chr> <dbl>
1 2024-03-13 19:17:10 2024-03-13 19:21:25 0 [IP Address] 76.122.42.81 100
2 2024-03-13 19:34:32 2024-03-13 19:45:01 0 [IP Address] 73.6.5.118 100
3 2024-03-13 21:12:43 2024-03-13 21:26:04 0 [IP Address] 92.119.18.122 100
4 2024-03-14 09:56:42 2024-03-14 10:03:37 0 [IP Address] 139.62.222.1… 100
5 2024-03-15 01:31:54 2024-03-15 15:07:03 0 [IP Address] 107.115.224.… 100
6 2024-03-14 11:15:41 2024-03-14 11:24:47 0 [IP Address] 65.87.105.57 97
7 2024-03-14 13:06:19 2024-03-14 13:25:21 0 [IP Address] 166.205.159.… 97
8 2024-03-22 12:31:08 2024-03-22 12:40:46 0 [IP Address] 104.28.32.246 100
9 2024-03-25 17:07:22 2024-03-25 17:15:07 0 [IP Address] 139.62.222.2… 100
10 2024-03-25 17:53:57 2024-03-25 18:05:40 0 [IP Address] 172.59.67.194 100
# ℹ 109 more rows
# ℹ 41 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>, Informed_Consent <dbl+lbl>, TP_1 <dbl+lbl>,
# TP_3 <dbl+lbl>, TP_4 <dbl+lbl>, TP_5 <dbl+lbl>, TP_2 <dbl+lbl>, …Create Test Performance DV
Rubric
- 4
- 3
- 2
- 1
- 4
- 4
- 3
(dataset.clean %>%
mutate(tp1 = case_when(TP_1 == 4 ~ 1,
TRUE ~ 0)) %>%
mutate(tp2 = case_when(TP_2 == 3 ~ 1,
TRUE ~ 0)) %>%
mutate(tp3 = case_when(TP_3 == 2 ~ 1,
TRUE ~ 0)) %>%
mutate(tp4 = case_when(TP_4 == 1 ~ 1,
TRUE ~ 0)) %>%
mutate(tp5 = case_when(TP_5 == 4 ~ 1,
TRUE ~ 0)) %>%
mutate(tp6 = case_when(TP_6 == 4 ~ 1,
TRUE ~ 0)) %>%
mutate(tp7 = case_when(TP_7 == 3 ~ 1,
TRUE ~ 0)) -> dataset.clean)# A tibble: 119 × 53
StartDate EndDate Status IPAddress Progress
<dttm> <dttm> <dbl+lbl> <chr> <dbl>
1 2024-03-13 19:17:10 2024-03-13 19:21:25 0 [IP Address] 76.122.42.81 100
2 2024-03-13 19:34:32 2024-03-13 19:45:01 0 [IP Address] 73.6.5.118 100
3 2024-03-13 21:12:43 2024-03-13 21:26:04 0 [IP Address] 92.119.18.122 100
4 2024-03-14 09:56:42 2024-03-14 10:03:37 0 [IP Address] 139.62.222.1… 100
5 2024-03-15 01:31:54 2024-03-15 15:07:03 0 [IP Address] 107.115.224.… 100
6 2024-03-14 11:15:41 2024-03-14 11:24:47 0 [IP Address] 65.87.105.57 97
7 2024-03-14 13:06:19 2024-03-14 13:25:21 0 [IP Address] 166.205.159.… 97
8 2024-03-22 12:31:08 2024-03-22 12:40:46 0 [IP Address] 104.28.32.246 100
9 2024-03-25 17:07:22 2024-03-25 17:15:07 0 [IP Address] 139.62.222.2… 100
10 2024-03-25 17:53:57 2024-03-25 18:05:40 0 [IP Address] 172.59.67.194 100
# ℹ 109 more rows
# ℹ 48 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>, Informed_Consent <dbl+lbl>, TP_1 <dbl+lbl>,
# TP_3 <dbl+lbl>, TP_4 <dbl+lbl>, TP_5 <dbl+lbl>, TP_2 <dbl+lbl>, …(dataset.clean %>%
rowwise()%>%
mutate(TP_totalCorrect = sum(tp1, tp2, tp3, tp4, tp5, tp6, tp7)) -> dataset.clean)# A tibble: 119 × 54
# Rowwise:
StartDate EndDate Status IPAddress Progress
<dttm> <dttm> <dbl+lbl> <chr> <dbl>
1 2024-03-13 19:17:10 2024-03-13 19:21:25 0 [IP Address] 76.122.42.81 100
2 2024-03-13 19:34:32 2024-03-13 19:45:01 0 [IP Address] 73.6.5.118 100
3 2024-03-13 21:12:43 2024-03-13 21:26:04 0 [IP Address] 92.119.18.122 100
4 2024-03-14 09:56:42 2024-03-14 10:03:37 0 [IP Address] 139.62.222.1… 100
5 2024-03-15 01:31:54 2024-03-15 15:07:03 0 [IP Address] 107.115.224.… 100
6 2024-03-14 11:15:41 2024-03-14 11:24:47 0 [IP Address] 65.87.105.57 97
7 2024-03-14 13:06:19 2024-03-14 13:25:21 0 [IP Address] 166.205.159.… 97
8 2024-03-22 12:31:08 2024-03-22 12:40:46 0 [IP Address] 104.28.32.246 100
9 2024-03-25 17:07:22 2024-03-25 17:15:07 0 [IP Address] 139.62.222.2… 100
10 2024-03-25 17:53:57 2024-03-25 18:05:40 0 [IP Address] 172.59.67.194 100
# ℹ 109 more rows
# ℹ 49 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>, Informed_Consent <dbl+lbl>, TP_1 <dbl+lbl>,
# TP_3 <dbl+lbl>, TP_4 <dbl+lbl>, TP_5 <dbl+lbl>, TP_2 <dbl+lbl>, …ANOVA
aov_ez(id = "ResponseId",
dv = "TP_totalCorrect",
data = dataset.clean,
between=c("PhysiologicalArousalIV", "MindfulnessIV"),
anova_table = list(es = "pes"))Converting to factor: PhysiologicalArousalIV, MindfulnessIVContrasts set to contr.sum for the following variables: PhysiologicalArousalIV, MindfulnessIVAnova Table (Type 3 tests)
Response: TP_totalCorrect
Effect df MSE F pes p.value
1 PhysiologicalArousalIV 1, 115 1.97 3.48 + .029 .065
2 MindfulnessIV 1, 115 1.97 0.38 .003 .540
3 PhysiologicalArousalIV:MindfulnessIV 1, 115 1.97 0.48 .004 .490
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1