Mindfulness Experiment Analysis

Load Packages

if (!require(haven)){
  install.packages("haven", dependencies = TRUE)
  require(haven)
}
Loading required package: haven
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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✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
if (!require(afex)){
  install.packages("afex", dependencies = TRUE)
  require(afex)
}
Loading required package: afex
Loading required package: lme4
Loading required package: Matrix

Attaching package: 'Matrix'
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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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Attaching package: 'afex'
The following object is masked from 'package:lme4':

    lmer
if (!require(summarytools)){
  install.packages("summarytools", dependencies = TRUE)
  require(summarytools)
}
Loading required package: summarytools
Warning in fun(libname, pkgname): couldn't connect to display ":0"
system might not have X11 capabilities; in case of errors when using dfSummary(), set st_options(use.x11 = FALSE)

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if (!require(psych)){
  install.packages("psych", dependencies = TRUE)
  require(psych)
}
Loading required package: psych

Attaching package: 'psych'
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    %+%, alpha

Import Data

dataset <- read_sav("PsyKicks Experiment Data.sav")

Cleaning Based on Progress

(dataset %>%
  filter(Progress > 79) -> dataset.cleaned)
# A tibble: 149 × 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 139 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.cleaned %>%
  filter(Duration__in_seconds_ > 120) -> dataset.cleaned)
# A tibble: 135 × 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 125 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>, …

Mindfulness Meditation IV

(dataset.cleaned %>%
  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.cleaned)
# A tibble: 135 × 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 125 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>, …

Physiological Arousal IV

(dataset.cleaned %>%
  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.cleaned)
# A tibble: 135 × 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 125 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>, …

Test Performance DV

Rubric

  1. 4
  2. 3
  3. 2
  4. 1
  5. 4
  6. 4
  7. 3
(dataset.cleaned %>% 
  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.cleaned)
# A tibble: 135 × 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 125 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.cleaned %>%
  rowwise()%>%
  mutate(TP_totalCorrect = sum(tp1, tp2, tp3, tp4, tp5, tp6, tp7)) -> dataset.cleaned)
# A tibble: 135 × 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 125 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>, …
aov_ez(id = "ResponseId", 
       dv = "TP_totalCorrect", 
       data = dataset.cleaned, 
       between=c("PhysiologicalArousalIV", "MindfulnessIV"),
       anova_table = list(es = "pes"))
Converting to factor: PhysiologicalArousalIV, MindfulnessIV
Contrasts set to contr.sum for the following variables: PhysiologicalArousalIV, MindfulnessIV
Anova Table (Type 3 tests)

Response: TP_totalCorrect
                                Effect     df  MSE      F   pes p.value
1               PhysiologicalArousalIV 1, 131 1.90 2.80 +  .021    .097
2                        MindfulnessIV 1, 131 1.90   0.01 <.001    .938
3 PhysiologicalArousalIV:MindfulnessIV 1, 131 1.90   0.20  .002    .658
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Study Design Table Numbers

Physiological Marginal Means

dataset.cleaned %>%
  group_by(PhysiologicalArousalIV) %>%
  summarise(mean = mean(TP_totalCorrect),
            sd = sd(TP_totalCorrect))
# A tibble: 2 × 3
  PhysiologicalArousalIV    mean    sd
  <chr>                    <dbl> <dbl>
1 No Physiological Arousal  5.79  1.27
2 Physiological Arousal     5.38  1.47

Mindfulness Marginal Means

dataset.cleaned %>%
  group_by(MindfulnessIV) %>%
  summarise(mean = mean(TP_totalCorrect),
            sd = sd(TP_totalCorrect))
# A tibble: 2 × 3
  MindfulnessIV   mean    sd
  <chr>          <dbl> <dbl>
1 Mindfulness      5.6  1.52
2 No Mindfulness   5.6  1.18

Cell Means

dataset.cleaned %>%
  group_by(PhysiologicalArousalIV, MindfulnessIV) %>%
  summarise(mean = mean(TP_totalCorrect),
            sd = sd(TP_totalCorrect))
`summarise()` has grouped output by 'PhysiologicalArousalIV'. You can override
using the `.groups` argument.
# A tibble: 4 × 4
# Groups:   PhysiologicalArousalIV [2]
  PhysiologicalArousalIV   MindfulnessIV   mean    sd
  <chr>                    <chr>          <dbl> <dbl>
1 No Physiological Arousal Mindfulness     5.83  1.36
2 No Physiological Arousal No Mindfulness  5.74  1.15
3 Physiological Arousal    Mindfulness     5.32  1.68
4 Physiological Arousal    No Mindfulness  5.45  1.21

Method Section Numbers

print(dfSummary(dataset.cleaned, graph.magnif = .75), method = 'render')
Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Data Frame Summary

dataset.cleaned

Dimensions: 135 x 54
Duplicates: 0
No Variable Label Stats / Values Freqs (% of Valid) Graph Valid Missing
1 StartDate [POSIXct, POSIXt] Start Date
min : 2024-03-13 19:17:10
med : 2024-04-03 12:29:24
max : 2024-04-11 13:27:11
range : 28d 18H 10M 1S
135 distinct values 135 (100.0%) 0 (0.0%)
2 EndDate [POSIXct, POSIXt] End Date
min : 2024-03-13 19:21:25
med : 2024-04-03 12:38:05
max : 2024-04-11 13:31:48
range : 28d 18H 10M 23S
135 distinct values 135 (100.0%) 0 (0.0%)
3 Status [haven_labelled, vctrs_vctr, double] Response Type 1 distinct value
0 : 135 ( 100.0% )
135 (100.0%) 0 (0.0%)
4 IPAddress [character] IP Address
1. 160.238.23.162
2. 139.62.223.175
3. 172.59.69.22
4. 174.228.164.170
5. 68.248.220.246
6. 99.51.103.251
7. 99.51.98.140
8. 104.28.132.122
9. 104.28.32.246
10. 107.115.224.33
[ 100 others ]
17 ( 12.6% )
5 ( 3.7% )
2 ( 1.5% )
2 ( 1.5% )
2 ( 1.5% )
2 ( 1.5% )
2 ( 1.5% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
100 ( 74.1% )
135 (100.0%) 0 (0.0%)
5 Progress [numeric] Progress
Mean (sd) : 99.9 (0.7)
min ≤ med ≤ max:
94 ≤ 100 ≤ 100
IQR (CV) : 0 (0)
94 : 1 ( 0.7% )
97 : 3 ( 2.2% )
100 : 131 ( 97.0% )
135 (100.0%) 0 (0.0%)
6 Duration__in_seconds_ [numeric] Duration (in seconds)
Mean (sd) : 4580.1 (25966.5)
min ≤ med ≤ max:
138 ≤ 502 ≤ 276253
IQR (CV) : 377 (5.7)
126 distinct values 135 (100.0%) 0 (0.0%)
7 Finished [haven_labelled, vctrs_vctr, double] Finished
Min : 0
Mean : 1
Max : 1
0 : 4 ( 3.0% )
1 : 131 ( 97.0% )
135 (100.0%) 0 (0.0%)
8 RecordedDate [POSIXct, POSIXt] Recorded Date
min : 2024-03-13 19:21:26
med : 2024-04-03 12:41:20
max : 2024-04-11 13:31:49
range : 28d 18H 10M 23S
135 distinct values 135 (100.0%) 0 (0.0%)
9 ResponseId [character] Response ID
1. R_10Cs3VUxYk6wzeN
2. R_10uEItLGwXpsKGV
3. R_11Z4EKSQgvqbAEV
4. R_13qLfOtABIZOVf8
5. R_142j4naibfKSIYV
6. R_1CHC90FhqvVoTC2
7. R_1GW775pfjAPZqNP
8. R_1hYJdnwWRbTMXN4
9. R_1jVWfUgZIkrCA0j
10. R_1Jxe4yCKn6TkO0f
[ 125 others ]
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
125 ( 92.6% )
135 (100.0%) 0 (0.0%)
10 RecipientLastName [character] Recipient Last Name
All empty strings
135 (100.0%) 0 (0.0%)
11 RecipientFirstName [character] Recipient First Name
All empty strings
135 (100.0%) 0 (0.0%)
12 RecipientEmail [character] Recipient Email
All empty strings
135 (100.0%) 0 (0.0%)
13 ExternalReference [character] External Data Reference
All empty strings
135 (100.0%) 0 (0.0%)
14 LocationLatitude [character] Location Latitude
1. 30.3007
2. 29.6669
3. 30.2183
4. 30.3958
5. (Empty string)
6. 28.6344
7. 29.6475
8. 30.2903
9. 30.3511
10. 30.3668
[ 43 others ]
34 ( 25.2% )
17 ( 12.6% )
9 ( 6.7% )
5 ( 3.7% )
4 ( 3.0% )
4 ( 3.0% )
4 ( 3.0% )
3 ( 2.2% )
3 ( 2.2% )
3 ( 2.2% )
49 ( 36.3% )
135 (100.0%) 0 (0.0%)
15 LocationLongitude [character] Location Longitude
1. -81.4421
2. -81.6579
3. -81.5621
4. -81.6893
5. (Empty string)
6. -81.6221
7. -82.404
8. -81.506
9. -81.5092
10. -81.5873
[ 43 others ]
34 ( 25.2% )
17 ( 12.6% )
9 ( 6.7% )
5 ( 3.7% )
4 ( 3.0% )
4 ( 3.0% )
4 ( 3.0% )
3 ( 2.2% )
3 ( 2.2% )
3 ( 2.2% )
49 ( 36.3% )
135 (100.0%) 0 (0.0%)
16 DistributionChannel [character] Distribution Channel
1. anonymous
2. social
134 ( 99.3% )
1 ( 0.7% )
135 (100.0%) 0 (0.0%)
17 UserLanguage [character] User Language 1. EN
135 ( 100.0% )
135 (100.0%) 0 (0.0%)
18 Informed_Consent [haven_labelled, vctrs_vctr, double] Informed Consent University of North Florida Department of Psychological Sciences Purpose of Research and Specific procedures to be used: In this study, you will be asked to watch two different videos and follow along with their instructions. After watching the videos, you will be asked to answer seven math questions. It should take approximately 10 minutes to complete. All answers will remain anonymous. Please answer the questions to the best of your ability. Duration of Participation: Your participation should take 10 minutes. Benefits to the Individual: Your participation in this research will contribute to the body of psychological knowledge about variables that effect test performance. You will have the opportunity to gain a deeper understanding of psychological research. Risks to the Individual: This study poses no risks greater than those encountered in daily social interactions. Anonymity: Strict anonymity of all data will be upheld. Your responses will remain anonymous and will not be ass 1 distinct value
1 : 135 ( 100.0% )
135 (100.0%) 0 (0.0%)
19 TP_1 [haven_labelled, vctrs_vctr, double] Joey had 6 siblings. All of them were born 2 years apart. The youngest is Chloe who is only 7 years old while Joey is the eldest. Calculate Joey’s age.
Mean (sd) : 3.5 (0.9)
min ≤ med ≤ max:
1 ≤ 4 ≤ 4
IQR (CV) : 0 (0.2)
1 : 4 ( 3.0% )
2 : 21 ( 15.6% )
3 : 7 ( 5.2% )
4 : 103 ( 76.3% )
135 (100.0%) 0 (0.0%)
20 TP_3 [haven_labelled, vctrs_vctr, double] 3x + 2 = 14, solve for x
Mean (sd) : 2 (0.4)
min ≤ med ≤ max:
1 ≤ 2 ≤ 4
IQR (CV) : 0 (0.2)
1 : 3 ( 2.2% )
2 : 126 ( 94.0% )
3 : 2 ( 1.5% )
4 : 3 ( 2.2% )
134 (99.3%) 1 (0.7%)
21 TP_4 [haven_labelled, vctrs_vctr, double] Solve the systems of equations: x + y = 8 and 2x - y = 10
Mean (sd) : 1.5 (0.9)
min ≤ med ≤ max:
1 ≤ 1 ≤ 4
IQR (CV) : 1 (0.6)
1 : 97 ( 71.9% )
2 : 9 ( 6.7% )
3 : 26 ( 19.3% )
4 : 3 ( 2.2% )
135 (100.0%) 0 (0.0%)
22 TP_5 [haven_labelled, vctrs_vctr, double] Two angles of a triangle measure 15° and 85°. What is the measure for the third angle?
Mean (sd) : 3.6 (0.8)
min ≤ med ≤ max:
1 ≤ 4 ≤ 4
IQR (CV) : 0 (0.2)
1 : 4 ( 3.0% )
2 : 17 ( 12.6% )
3 : 12 ( 8.9% )
4 : 102 ( 75.6% )
135 (100.0%) 0 (0.0%)
23 TP_2 [haven_labelled, vctrs_vctr, double] Brian is a window cleaner. He uses the following formula to calculate the amount to charge (C) his customers: C = $20 + 4n where “n” is the number of windows a house has. If a house has 7 windows, how much would Brian charge?
Mean (sd) : 2.9 (0.5)
min ≤ med ≤ max:
1 ≤ 3 ≤ 4
IQR (CV) : 0 (0.2)
1 : 9 ( 6.7% )
2 : 4 ( 3.0% )
3 : 120 ( 88.9% )
4 : 2 ( 1.5% )
135 (100.0%) 0 (0.0%)
24 TP_6 [haven_labelled, vctrs_vctr, double] Distribute correctly: 4(6x+4)
Mean (sd) : 3.8 (0.7)
min ≤ med ≤ max:
1 ≤ 4 ≤ 4
IQR (CV) : 0 (0.2)
1 : 2 ( 1.5% )
2 : 12 ( 8.9% )
3 : 1 ( 0.7% )
4 : 120 ( 88.9% )
135 (100.0%) 0 (0.0%)
25 TP_7 [haven_labelled, vctrs_vctr, double] Solve the linear inequality 2x - 5 > -x + 4
Mean (sd) : 2.9 (0.7)
min ≤ med ≤ max:
1 ≤ 3 ≤ 4
IQR (CV) : 0 (0.2)
1 : 8 ( 5.9% )
2 : 19 ( 14.1% )
3 : 88 ( 65.2% )
4 : 20 ( 14.8% )
135 (100.0%) 0 (0.0%)
26 Gender [character] What gender do you identify with?
1. Female
2. Male
3. female
4. male
5. Man
6. Nonbinary
7. ALPHA MALE
8. Gangster
9. heterosexual
10. Non-binary
[ 5 others ]
66 ( 48.9% )
39 ( 28.9% )
10 ( 7.4% )
7 ( 5.2% )
2 ( 1.5% )
2 ( 1.5% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
5 ( 3.7% )
135 (100.0%) 0 (0.0%)
27 Race [character] What is your race/ethnicity?
1. White
2. Hispanic
3. white
4. Black
5. Caucasian
6. Asian
7. African American
8. African
9. White/hispanic
10. (Empty string)
[ 26 others ]
58 ( 43.0% )
12 ( 8.9% )
11 ( 8.1% )
8 ( 5.9% )
8 ( 5.9% )
4 ( 3.0% )
3 ( 2.2% )
2 ( 1.5% )
2 ( 1.5% )
1 ( 0.7% )
26 ( 19.3% )
135 (100.0%) 0 (0.0%)
28 Age [haven_labelled, vctrs_vctr, double] How old are you?
Mean (sd) : 3.6 (2.1)
min ≤ med ≤ max:
1 ≤ 3 ≤ 9
IQR (CV) : 3 (0.6)
1 : 28 ( 20.7% )
2 : 19 ( 14.1% )
3 : 25 ( 18.5% )
4 : 18 ( 13.3% )
5 : 21 ( 15.6% )
6 : 9 ( 6.7% )
7 : 9 ( 6.7% )
8 : 3 ( 2.2% )
9 : 3 ( 2.2% )
135 (100.0%) 0 (0.0%)
29 Level_of_Education [haven_labelled, vctrs_vctr, double] What is your level of education?
Mean (sd) : 1.3 (0.6)
min ≤ med ≤ max:
1 ≤ 1 ≤ 3
IQR (CV) : 0 (0.4)
1 : 101 ( 75.9% )
2 : 24 ( 18.0% )
3 : 8 ( 6.0% )
133 (98.5%) 2 (1.5%)
30 Workout_Frequency [haven_labelled, vctrs_vctr, double] How often do you workout?
Mean (sd) : 2 (0.7)
min ≤ med ≤ max:
1 ≤ 2 ≤ 3
IQR (CV) : 0 (0.3)
1 : 30 ( 22.2% )
2 : 77 ( 57.0% )
3 : 28 ( 20.7% )
135 (100.0%) 0 (0.0%)
31 Meditation_Frequency [haven_labelled, vctrs_vctr, double] How often do you participate in meditation exercises?
Mean (sd) : 1.4 (0.7)
min ≤ med ≤ max:
1 ≤ 1 ≤ 3
IQR (CV) : 1 (0.5)
1 : 97 ( 71.9% )
2 : 25 ( 18.5% )
3 : 13 ( 9.6% )
135 (100.0%) 0 (0.0%)
32 Hypotheses [character] What do you think the hypotheses of this study were?
1. (Empty string)
2. I don't know
3. Idk
4. no clue
5. No clue
6.  
·
physical exertion effec
7. Ability to perform comple
8. Adult males will show the
9. Are people willing to par
10. Are the people both menta
[ 111 others ]
9 ( 6.7% )
3 ( 2.2% )
3 ( 2.2% )
2 ( 1.5% )
2 ( 1.5% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
1 ( 0.7% )
111 ( 82.2% )
135 (100.0%) 0 (0.0%)
33 FL_10_DO_NoMindfulnessMeditation_PhysiologicalArousal [numeric] FL_10 - Block Randomizer - Display Order NoMindfulnessMeditation/PhysiologicalArousal 1 distinct value
1 : 29 ( 100.0% )
29 (21.5%) 106 (78.5%)
34 FL_10_DO_NoMindfulnessMeditation_NoPhysiologicalArousal [numeric] FL_10 - Block Randomizer - Display Order NoMindfulnessMeditation/NoPhysiologicalArousal 1 distinct value
1 : 31 ( 100.0% )
31 (23.0%) 104 (77.0%)
35 FL_10_DO_MindfulnessMeditation_PhysiologicalArousal [numeric] FL_10 - Block Randomizer - Display Order MindfulnessMeditation/PhysiologicalArousal 1 distinct value
1 : 34 ( 100.0% )
34 (25.2%) 101 (74.8%)
36 FL_10_DO_MindfulnessMeditation_NoPhysiologicalArousal [numeric] FL_10 - Block Randomizer - Display Order MindfulnessMeditation/NoPhysiologicalArousal 1 distinct value
1 : 41 ( 100.0% )
41 (30.4%) 94 (69.6%)
37 TestPerformance_DO_TP_1 [numeric] Test Performance - Display Order TP 1
Mean (sd) : 5.1 (2)
min ≤ med ≤ max:
2 ≤ 5 ≤ 8
IQR (CV) : 4 (0.4)
2 : 16 ( 11.9% )
3 : 23 ( 17.0% )
4 : 16 ( 11.9% )
5 : 16 ( 11.9% )
6 : 24 ( 17.8% )
7 : 19 ( 14.1% )
8 : 21 ( 15.6% )
135 (100.0%) 0 (0.0%)
38 TestPerformance_DO_TP_6 [numeric] Test Performance - Display Order TP 6
Mean (sd) : 5.3 (2.1)
min ≤ med ≤ max:
2 ≤ 5 ≤ 8
IQR (CV) : 3 (0.4)
2 : 15 ( 11.1% )
3 : 17 ( 12.6% )
4 : 21 ( 15.6% )
5 : 19 ( 14.1% )
6 : 17 ( 12.6% )
7 : 16 ( 11.9% )
8 : 30 ( 22.2% )
135 (100.0%) 0 (0.0%)
39 TestPerformance_DO_TP_Instructions [numeric] Test Performance - Display Order TP Instructions 1 distinct value
1 : 135 ( 100.0% )
135 (100.0%) 0 (0.0%)
40 TestPerformance_DO_TP_3 [numeric] Test Performance - Display Order TP 3
Mean (sd) : 4.5 (1.9)
min ≤ med ≤ max:
2 ≤ 5 ≤ 8
IQR (CV) : 3 (0.4)
2 : 24 ( 17.8% )
3 : 27 ( 20.0% )
4 : 16 ( 11.9% )
5 : 26 ( 19.3% )
6 : 16 ( 11.9% )
7 : 19 ( 14.1% )
8 : 7 ( 5.2% )
135 (100.0%) 0 (0.0%)
41 TestPerformance_DO_TP_7 [numeric] Test Performance - Display Order TP 7
Mean (sd) : 4.7 (1.9)
min ≤ med ≤ max:
2 ≤ 5 ≤ 8
IQR (CV) : 3 (0.4)
2 : 23 ( 17.0% )
3 : 21 ( 15.6% )
4 : 19 ( 14.1% )
5 : 16 ( 11.9% )
6 : 26 ( 19.3% )
7 : 20 ( 14.8% )
8 : 10 ( 7.4% )
135 (100.0%) 0 (0.0%)
42 TestPerformance_DO_TP_2 [numeric] Test Performance - Display Order TP 2
Mean (sd) : 5 (2)
min ≤ med ≤ max:
2 ≤ 5 ≤ 8
IQR (CV) : 4 (0.4)
2 : 19 ( 14.1% )
3 : 16 ( 11.9% )
4 : 23 ( 17.0% )
5 : 24 ( 17.8% )
6 : 16 ( 11.9% )
7 : 17 ( 12.6% )
8 : 20 ( 14.8% )
135 (100.0%) 0 (0.0%)
43 TestPerformance_DO_TP_5 [numeric] Test Performance - Display Order TP 5
Mean (sd) : 5.1 (2.1)
min ≤ med ≤ max:
2 ≤ 5 ≤ 8
IQR (CV) : 4 (0.4)
2 : 21 ( 15.6% )
3 : 14 ( 10.4% )
4 : 20 ( 14.8% )
5 : 22 ( 16.3% )
6 : 13 ( 9.6% )
7 : 19 ( 14.1% )
8 : 26 ( 19.3% )
135 (100.0%) 0 (0.0%)
44 TestPerformance_DO_TP_4 [numeric] Test Performance - Display Order TP 4
Mean (sd) : 5.2 (2)
min ≤ med ≤ max:
2 ≤ 6 ≤ 8
IQR (CV) : 3.5 (0.4)
2 : 17 ( 12.6% )
3 : 17 ( 12.6% )
4 : 20 ( 14.8% )
5 : 12 ( 8.9% )
6 : 23 ( 17.0% )
7 : 25 ( 18.5% )
8 : 21 ( 15.6% )
135 (100.0%) 0 (0.0%)
45 MindfulnessIV [character]
1. Mindfulness
2. No Mindfulness
75 ( 55.6% )
60 ( 44.4% )
135 (100.0%) 0 (0.0%)
46 PhysiologicalArousalIV [character]
1. No Physiological Arousal
2. Physiological Arousal
72 ( 53.3% )
63 ( 46.7% )
135 (100.0%) 0 (0.0%)
47 tp1 [numeric]
Min : 0
Mean : 0.8
Max : 1
0 : 32 ( 23.7% )
1 : 103 ( 76.3% )
135 (100.0%) 0 (0.0%)
48 tp2 [numeric]
Min : 0
Mean : 0.9
Max : 1
0 : 15 ( 11.1% )
1 : 120 ( 88.9% )
135 (100.0%) 0 (0.0%)
49 tp3 [numeric]
Min : 0
Mean : 0.9
Max : 1
0 : 9 ( 6.7% )
1 : 126 ( 93.3% )
135 (100.0%) 0 (0.0%)
50 tp4 [numeric]
Min : 0
Mean : 0.7
Max : 1
0 : 38 ( 28.1% )
1 : 97 ( 71.9% )
135 (100.0%) 0 (0.0%)
51 tp5 [numeric]
Min : 0
Mean : 0.8
Max : 1
0 : 33 ( 24.4% )
1 : 102 ( 75.6% )
135 (100.0%) 0 (0.0%)
52 tp6 [numeric]
Min : 0
Mean : 0.9
Max : 1
0 : 15 ( 11.1% )
1 : 120 ( 88.9% )
135 (100.0%) 0 (0.0%)
53 tp7 [numeric]
Min : 0
Mean : 0.7
Max : 1
0 : 47 ( 34.8% )
1 : 88 ( 65.2% )
135 (100.0%) 0 (0.0%)
54 TP_totalCorrect [numeric]
Mean (sd) : 5.6 (1.4)
min ≤ med ≤ max:
1 ≤ 6 ≤ 7
IQR (CV) : 2 (0.2)
1 : 2 ( 1.5% )
2 : 3 ( 2.2% )
3 : 6 ( 4.4% )
4 : 13 ( 9.6% )
5 : 30 ( 22.2% )
6 : 39 ( 28.9% )
7 : 42 ( 31.1% )
135 (100.0%) 0 (0.0%)

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2024-04-11