The soft launch gave 27 responses. The column ‘n’ refers to the number of participants and the percentage is next to this.
Assignment to question framing (die vs. recover) looks good as does assignment to the three experimental conditions
## # A tibble: 2 x 3
## Frame n percent
## <chr> <int> <chr>
## 1 Die 14 51.9%
## 2 Recover 13 48.1%## # A tibble: 3 x 3
## Group n percent
## <fct> <int> <chr>
## 1 Control 8 29.6%
## 2 CDC 10 37.0%
## 3 KS 9 33.3%The age quota seems off (we ask for around 30% in each), but maybe this is because the soft launch is only with 27 participants?
## # A tibble: 3 x 3
## Age_group n percent
## <chr> <int> <chr>
## 1 18 to 34 4 14.8%
## 2 35 to 54 9 33.3%
## 3 55 and older 14 51.9%The Gender quota looks okay
## # A tibble: 2 x 3
## GenderCHR n percent
## <chr> <int> <chr>
## 1 Female 14 51.9%
## 2 Male or Other Gender 13 48.1%The Race/Ethnicity quota looks a little bit off. We asked for: Non-Hispanic White 62.3% (for soft launch = 67%) Non-Hispanic Black 12.4% (for soft launch = 15%) Hispanic 17.3% (for soft launch = 11%) Asian/Other Race 8% (for soft launch = 7%)
## The following `from` values were not present in `x`: 1, 4, 6## # A tibble: 5 x 4
## # Groups: HispCHR [2]
## HispCHR RaceCHR n percent
## <chr> <chr> <int> <chr>
## 1 Hispanic Asian or Asian American 1 3.7%
## 2 Hispanic White or Euorpean American 2 7.4%
## 3 Non-hispanic Asian or Asian American 4 14.8%
## 4 Non-hispanic Black or African American 2 7.4%
## 5 Non-hispanic White or Euorpean American 18 66.7%The Income quota looks a bit off too. We asked for 40%, 33% and 27%.
## # A tibble: 3 x 3
## Income_group n percent
## <fct> <int> <chr>
## 1 $0 - $49k 20 74.1%
## 2 $50K to $99K 5 18.5%
## 3 $100K and more 2 7.4%We don’t have quota for region.
## # A tibble: 4 x 2
## region n
## <chr> <int>
## 1 midwest 7
## 2 northeast 4
## 3 south 10
## 4 west 6This is to check those who consent and for speeders, who are defined as those who complete in one-half the median soft launch time.
## # A tibble: 1 x 2
## Consent n
## <chr> <int>
## 1 1 27## # A tibble: 1 x 2
## term n
## <chr> <int>
## 1 "" 27One-half the median soft launch time (12 mins) would be 6 minutes.
df$`Duration (in seconds)` <- sapply(df$`Duration (in seconds)`,as.numeric)
median(df$`Duration (in seconds)`)## [1] 1003seconds_to_period(731) #Median duration is 12 minutes## [1] "12M 11S"mean(df$`Duration (in seconds)`)## [1] 1074.259seconds_to_period(859) #Mean time is 14 minutes## [1] "14M 19S"Checking CV19 Ageism questions. Looking at min to max values they look okay.
## vars n mean sd median trimmed mad min max range
## Ageism_CVBelief_1 1 27 1.89 1.09 2.0 1.78 1.48 1 4 3
## Ageism_CVBelief_2 2 27 3.15 0.95 3.0 3.26 1.48 1 4 3
## Ageism_CVBelief_3 3 27 3.59 0.75 4.0 3.74 0.00 1 4 3
## Ageism_CVBelief_4 4 27 1.93 1.24 1.0 1.83 0.00 1 4 3
## Ageism_CVBelief_5 5 27 2.04 1.16 2.0 1.96 1.48 1 4 3
## Ageism_CVBelief6a_1 6 14 33.14 32.28 23.5 31.17 28.17 1 89 88
## Ageism_CVBelief7a_1 7 13 44.23 28.10 41.0 44.55 35.58 3 82 79
## Ageism_CVBelief6b_1 8 13 61.08 23.07 60.0 61.45 23.72 19 99 80
## Ageism_CVBelief7b_1 9 13 50.62 26.58 50.0 49.55 29.65 15 98 83
## Ageism_CVPriority1 10 27 3.19 1.92 3.0 3.13 2.97 1 6 5
## Ageism_CVPriority2_1 11 27 2.81 1.82 2.0 2.70 1.48 1 6 5
## Ageism_CVPriority2_2 12 27 3.00 1.80 3.0 2.91 2.97 1 6 5
## skew kurtosis se
## Ageism_CVBelief_1 0.90 -0.59 0.21
## Ageism_CVBelief_2 -0.80 -0.45 0.18
## Ageism_CVBelief_3 -1.90 3.28 0.14
## Ageism_CVBelief_4 0.72 -1.27 0.24
## Ageism_CVBelief_5 0.50 -1.37 0.22
## Ageism_CVBelief6a_1 0.66 -1.18 8.63
## Ageism_CVBelief7a_1 -0.11 -1.64 7.79
## Ageism_CVBelief6b_1 0.07 -1.01 6.40
## Ageism_CVBelief7b_1 0.28 -1.15 7.37
## Ageism_CVPriority1 0.25 -1.52 0.37
## Ageism_CVPriority2_1 0.45 -1.33 0.35
## Ageism_CVPriority2_2 0.27 -1.38 0.35Checking CV19 Adherence questions. Looking at min to max values for Adhere intent it should be 1-6. I have recoded them in Qualtrics, but not published the changes and will ask the best way to proceed at this stage.
## vars n mean sd median trimmed mad min max range skew
## Adhere_willing 1 27 4.70 1.54 6 4.83 0.00 2 6 4 -0.55
## AdhereIntent_1 2 27 2.89 2.19 2 2.70 1.48 1 7 6 0.79
## AdhereIntent_2 3 27 3.11 1.85 4 2.96 1.48 1 7 6 0.37
## AdhereIntent_3 4 27 2.26 1.70 2 1.96 1.48 1 7 6 1.51
## AdhereIntent_4 5 27 2.74 2.05 2 2.52 1.48 1 7 6 0.93
## AdhereIntent_5 6 27 2.59 1.89 2 2.35 1.48 1 7 6 1.17
## AdhereIntent_6 7 27 5.85 1.88 7 6.09 0.00 2 7 5 -1.14
## AdhereIntent_7 8 27 6.19 1.44 7 6.39 0.00 2 7 5 -1.42
## AdhereIntent_8 9 27 4.96 1.79 4 5.09 2.97 1 7 6 -0.30
## AdhereAttitude1_1 10 27 4.33 1.00 5 4.43 0.00 2 5 3 -0.89
## AdhereAttitude2 11 27 4.33 1.86 4 4.39 1.48 1 7 6 0.01
## AdhereAttitude3_1 12 27 4.78 1.53 6 4.96 0.00 1 6 5 -0.82
## AdhereAttitude3_2 13 27 4.81 1.36 5 4.96 1.48 2 6 4 -0.83
## AdhereAttitude3_3 14 27 4.74 1.35 5 4.87 1.48 2 6 4 -0.54
## kurtosis se
## Adhere_willing -1.40 0.30
## AdhereIntent_1 -0.93 0.42
## AdhereIntent_2 -0.73 0.36
## AdhereIntent_3 1.71 0.33
## AdhereIntent_4 -0.45 0.39
## AdhereIntent_5 0.20 0.36
## AdhereIntent_6 -0.46 0.36
## AdhereIntent_7 0.67 0.28
## AdhereIntent_8 -1.11 0.34
## AdhereAttitude1_1 -0.95 0.19
## AdhereAttitude2 -0.83 0.36
## AdhereAttitude3_1 -0.65 0.29
## AdhereAttitude3_2 -0.56 0.26
## AdhereAttitude3_3 -1.06 0.26Checking Collective trust (will rename this for any official write up) social norm and prosocial questions. Looking at min to max values for coll trust (2,3) and social norm (1,2,3) these should all be 1-6. I have recoded them in Qualtrics, but not published the changes and will ask the best way to proceed at this stage.
## vars n mean sd median trimmed mad min max range skew
## Coll_Trust1_1 1 27 3.26 1.23 3.0 3.30 1.48 1 5 4 0.00
## Coll_Trust2 2 27 4.37 1.90 5.0 4.43 2.97 1 7 6 -0.35
## Coll_Trust3 3 27 5.81 1.44 6.0 6.00 1.48 2 7 5 -1.02
## Coll_Trust4_1 4 27 58.00 25.08 57.0 58.39 23.72 10 100 90 -0.12
## Socialnorm_1 5 27 5.67 1.78 7.0 5.87 0.00 2 7 5 -0.94
## Socialnorm_2 6 27 5.63 1.55 6.0 5.83 1.48 1 7 6 -1.31
## Socialnorm_3 7 27 5.37 1.78 6.0 5.57 1.48 1 7 6 -0.86
## ProSocial_1 8 27 5.26 2.25 7.0 5.39 1.48 1 8 7 -0.61
## ProSocial_2 9 26 4.46 2.06 4.5 4.50 2.22 1 8 7 -0.13
## ProSocial_3 10 27 5.63 1.98 6.0 5.74 1.48 2 8 6 -0.50
## ProSocial_4 11 27 5.63 2.17 7.0 5.83 1.48 1 8 7 -0.95
## ProSocial_5 12 27 5.56 2.22 7.0 5.74 1.48 1 8 7 -0.69
## ProSocial_6 13 27 5.22 1.95 6.0 5.35 1.48 1 8 7 -0.45
## kurtosis se
## Coll_Trust1_1 -1.03 0.24
## Coll_Trust2 -1.08 0.37
## Coll_Trust3 0.14 0.28
## Coll_Trust4_1 -0.82 4.83
## Socialnorm_1 -0.73 0.34
## Socialnorm_2 1.10 0.30
## Socialnorm_3 -0.44 0.34
## ProSocial_1 -1.02 0.43
## ProSocial_2 -1.10 0.40
## ProSocial_3 -1.28 0.38
## ProSocial_4 -0.34 0.42
## ProSocial_5 -0.95 0.43
## ProSocial_6 -1.16 0.37Checking individual risk and societal risk questions. 23 had covid so only 23 answered individual risk questions. Looking at min to max values, these should all be 1-6 (expect the HadCov1 question) so we have an issue with societal risk 1 and individual risk 9. Again, I have recoded these in Qualtrics, but not published it and will ask the best way to proceed at this stage.
## vars n mean sd median trimmed mad min max range skew
## SocietalRisk1 1 27 10.78 2.26 12 11.17 0.00 2 12 10 -2.32
## SocietalRisk2 2 27 3.93 1.86 4 3.91 1.48 1 7 6 0.07
## kurtosis se
## SocietalRisk1 5.78 0.43
## SocietalRisk2 -1.12 0.36## # A tibble: 3 x 3
## HadCov1 n percent
## <chr> <int> <chr>
## 1 Currently have covid 3 11.1%
## 2 Had covid and recovered 1 3.7%
## 3 Haven't had covid 23 85.2%## vars n mean sd median trimmed mad min max range skew kurtosis
## IndivRisk1 1 23 2.22 1.59 2 1.95 1.48 1 7 6 1.33 1.23
## IndivRisk2 2 23 3.65 2.31 3 3.58 2.97 1 7 6 0.20 -1.54
## IndivRisk3 3 23 3.35 2.25 2 3.21 1.48 1 7 6 0.50 -1.33
## IndivRisk4 4 23 5.04 2.40 6 5.26 1.48 1 7 6 -0.71 -1.23
## IndivRisk5 5 23 4.74 2.47 6 4.89 1.48 1 7 6 -0.54 -1.51
## IndivRisk6 6 23 5.00 2.41 6 5.21 1.48 1 7 6 -0.74 -1.26
## IndivRisk7 7 23 4.43 2.19 5 4.53 2.97 1 7 6 -0.39 -1.36
## IndivRisk8 8 23 4.04 2.12 4 4.05 2.97 1 7 6 -0.03 -1.42
## IndivRisk9 9 23 6.91 3.09 6 7.11 5.93 2 10 8 -0.29 -1.56
## se
## IndivRisk1 0.33
## IndivRisk2 0.48
## IndivRisk3 0.47
## IndivRisk4 0.50
## IndivRisk5 0.52
## IndivRisk6 0.50
## IndivRisk7 0.46
## IndivRisk8 0.44
## IndivRisk9 0.64Checking CDC trust questions. Looking at min to max values they look okay.
## vars n mean sd median trimmed mad min max range skew kurtosis
## CDCTrust1 1 27 3.78 1.28 4 3.87 1.48 1 5 4 -0.55 -1.14
## CDCTrust2 2 27 3.81 1.11 4 3.87 1.48 2 5 3 -0.46 -1.20
## CDCTrust3 3 27 3.70 1.17 4 3.74 1.48 2 5 3 -0.27 -1.48
## se
## CDCTrust1 0.25
## CDCTrust2 0.21
## CDCTrust3 0.23Checking medical history questions. The COVatRisk questions were coded (4,5,6) instead of (1,2,3). Again I have recoded, but not published and will ask. For KnowCov1, the numbers are just where people put more than one answer.
## The following `from` values were not present in `x`: 6## # A tibble: 2 x 3
## CoVatRisk n percent
## <chr> <int> <chr>
## 1 No 14 51.9%
## 2 Yes 13 48.1%## # A tibble: 8 x 3
## KnowCov1 n percent
## <chr> <int> <chr>
## 1 1,5 1 3.7%
## 2 3,4 1 3.7%
## 3 3,5 1 3.7%
## 4 No 16 59.3%
## 5 Yes immediate family 2 7.4%
## 6 yes other family 1 3.7%
## 7 yes, co-worker 2 7.4%
## 8 yes, friend 3 11.1%## # A tibble: 3 x 3
## KnowCov2 n percent
## <chr> <int> <chr>
## 1 don't know 3 11.1%
## 2 No 4 14.8%
## 3 Yes 20 74.1%Checking Demographics questions. Values look okay.
## The following `from` values were not present in `x`: 6## # A tibble: 5 x 3
## RuralSub n percent
## <chr> <int> <chr>
## 1 large city more than 1million 3 11.1%
## 2 Mid sized city (100,000 to 1million) 5 18.5%
## 3 Rural 5 18.5%
## 4 Small (less than 100,000) 6 22.2%
## 5 Suburban near large city 8 29.6%## # A tibble: 3 x 3
## MedWork n percent
## <chr> <int> <chr>
## 1 "" 1 3.7%
## 2 "No" 24 88.9%
## 3 "Yes" 2 7.4%## [1] "" "" "" ""
## [5] "Home health care" "" "" ""
## [9] "" "" "" ""
## [13] "" "" "" ""
## [17] "" "" "" ""
## [21] "" "" "" ""
## [25] "" "" ""Checking Ageism facts questions. Looking at min to max values they look okay.
## vars n mean sd median trimmed mad min max range skew
## Ageism_Facts1_1 1 27 1.26 0.45 1 1.22 0 1 2 1 1.04
## Ageism_Facts1_2 2 27 1.70 0.47 2 1.74 0 1 2 1 -0.84
## Ageism_Facts1_3 3 27 1.26 0.45 1 1.22 0 1 2 1 1.04
## Ageism_Facts1_4 4 27 1.96 0.19 2 2.00 0 1 2 1 -4.63
## Ageism_Facts1_5 5 27 1.59 0.50 2 1.61 0 1 2 1 -0.36
## Ageism_Facts1_6 6 27 1.85 0.36 2 1.91 0 1 2 1 -1.87
## Ageism_Facts2_1 7 27 1.48 0.51 1 1.48 0 1 2 1 0.07
## Ageism_Facts2_2 8 27 1.56 0.51 2 1.57 0 1 2 1 -0.21
## Ageism_Facts2_3 9 27 1.56 0.51 2 1.57 0 1 2 1 -0.21
## Ageism_Facts2_4 10 27 1.33 0.48 1 1.30 0 1 2 1 0.67
## Ageism_Facts2_5 11 27 1.44 0.51 1 1.43 0 1 2 1 0.21
## Ageism_Facts2_6 12 27 1.56 0.51 2 1.57 0 1 2 1 -0.21
## kurtosis se
## Ageism_Facts1_1 -0.95 0.09
## Ageism_Facts1_2 -1.33 0.09
## Ageism_Facts1_3 -0.95 0.09
## Ageism_Facts1_4 20.22 0.04
## Ageism_Facts1_5 -1.94 0.10
## Ageism_Facts1_6 1.57 0.07
## Ageism_Facts2_1 -2.07 0.10
## Ageism_Facts2_2 -2.03 0.10
## Ageism_Facts2_3 -2.03 0.10
## Ageism_Facts2_4 -1.61 0.09
## Ageism_Facts2_5 -2.03 0.10
## Ageism_Facts2_6 -2.03 0.10Checking Ageism Fraboni questions. Looking at min to max values they look okay.
## vars n mean sd median trimmed mad min max range skew
## Ageism_Fabroni1_1 1 27 2.04 0.90 2 1.96 1.48 1 4 3 0.55
## Ageism_Fabroni1_2 2 27 2.19 0.88 2 2.13 1.48 1 4 3 0.31
## Ageism_Fabroni1_3 3 27 2.44 0.85 2 2.43 1.48 1 4 3 0.17
## Ageism_Fabroni1_4 4 27 2.00 0.88 2 1.91 1.48 1 4 3 0.66
## Ageism_Fabroni1_5 5 27 2.63 0.69 3 2.57 1.48 2 4 2 0.57
## Ageism_Fabroni1_6 6 27 2.22 0.97 2 2.17 1.48 1 4 3 0.53
## Ageism_Fabroni2_1 7 27 2.26 0.81 2 2.22 0.00 1 4 3 0.77
## Ageism_Fabroni2_2 8 27 2.33 0.92 2 2.30 1.48 1 4 3 0.19
## Ageism_Fabroni2_3 9 27 2.56 0.89 2 2.57 1.48 1 4 3 0.31
## Ageism_Fabroni2_4 10 27 2.04 0.98 2 1.96 1.48 1 4 3 0.40
## Ageism_Fabroni2_5 11 27 1.81 1.04 1 1.70 0.00 1 4 3 0.95
## Ageism_Fabroni2_6 12 27 1.81 0.88 2 1.70 1.48 1 4 3 1.00
## Ageism_Fabroni3_1 13 27 1.96 1.02 2 1.87 1.48 1 4 3 0.70
## Ageism_Fabroni3_2 14 27 2.19 0.88 2 2.13 1.48 1 4 3 0.31
## Ageism_Fabroni3_3 15 27 2.15 0.91 2 2.09 0.00 1 4 3 0.61
## Ageism_Fabroni3_4 16 27 3.30 0.82 3 3.43 1.48 1 4 3 -1.35
## Ageism_Fabroni3_5 17 27 2.26 1.10 2 2.22 1.48 1 4 3 0.35
## Ageism_Fabroni3_6 18 27 2.00 1.21 1 1.91 0.00 1 4 3 0.63
## Ageism_Fabroni4_1 19 27 3.15 0.82 3 3.26 0.00 1 4 3 -1.07
## Ageism_Fabroni4_2 20 27 3.33 0.62 3 3.39 0.00 2 4 2 -0.31
## Ageism_Fabroni4_3 21 27 3.19 0.83 3 3.30 0.00 1 4 3 -1.10
## Ageism_Fabroni4_4 22 27 3.26 0.98 4 3.39 0.00 1 4 3 -1.21
## Ageism_Fabroni4_5 23 27 2.04 1.02 2 1.96 1.48 1 4 3 0.56
## kurtosis se
## Ageism_Fabroni1_1 -0.54 0.17
## Ageism_Fabroni1_2 -0.72 0.17
## Ageism_Fabroni1_3 -0.70 0.16
## Ageism_Fabroni1_4 -0.25 0.17
## Ageism_Fabroni1_5 -0.88 0.13
## Ageism_Fabroni1_6 -0.74 0.19
## Ageism_Fabroni2_1 0.08 0.16
## Ageism_Fabroni2_2 -0.90 0.18
## Ageism_Fabroni2_3 -0.95 0.17
## Ageism_Fabroni2_4 -1.10 0.19
## Ageism_Fabroni2_5 -0.44 0.20
## Ageism_Fabroni2_6 0.38 0.17
## Ageism_Fabroni3_1 -0.74 0.20
## Ageism_Fabroni3_2 -0.72 0.17
## Ageism_Fabroni3_3 -0.39 0.17
## Ageism_Fabroni3_4 1.72 0.16
## Ageism_Fabroni3_5 -1.26 0.21
## Ageism_Fabroni3_6 -1.30 0.23
## Ageism_Fabroni4_1 1.05 0.16
## Ageism_Fabroni4_2 -0.83 0.12
## Ageism_Fabroni4_3 0.99 0.16
## Ageism_Fabroni4_4 0.35 0.19
## Ageism_Fabroni4_5 -0.92 0.20Checking Social and self values questions. Looking at min to max values they look okay.
## vars n mean sd median trimmed mad min max range skew
## Social_Values_1 1 27 3.89 1.05 4 3.96 1.48 2 5 3 -0.36
## Social_Values_2 2 27 4.11 0.93 4 4.17 1.48 2 5 3 -0.48
## Social_Values_3 3 27 3.96 1.13 4 4.09 1.48 1 5 4 -0.86
## Self_Values_1 4 27 2.93 1.21 3 2.91 1.48 1 5 4 0.39
## Self_Values_2 5 27 3.07 1.57 3 3.09 2.97 1 5 4 -0.12
## kurtosis se
## Social_Values_1 -1.24 0.20
## Social_Values_2 -1.15 0.18
## Social_Values_3 -0.15 0.22
## Self_Values_1 -1.00 0.23
## Self_Values_2 -1.57 0.30Checking Political and religious questions. Looking at min to max values they look okay.
## vars n mean sd median trimmed mad min max range skew
## PolApproval_1 1 27 2.37 1.55 2.0 2.26 1.48 1 5 4 0.59
## PolApproval_2 2 27 3.48 1.25 4.0 3.52 1.48 1 5 4 -0.24
## PolApproval_3 3 27 3.11 1.15 3.0 3.13 1.48 1 5 4 -0.21
## PolApproval_4 4 27 3.07 1.14 3.0 3.09 1.48 1 5 4 -0.29
## PolApproval_5 5 27 3.63 1.28 4.0 3.74 1.48 1 5 4 -0.71
## PolGov_1 6 26 51.96 28.33 50.5 51.77 33.36 6 100 94 0.00
## PolEcon 7 27 4.22 2.08 4.0 4.26 2.97 1 7 6 -0.24
## PolSoc 8 27 4.19 2.00 4.0 4.22 2.97 1 7 6 -0.33
## kurtosis se
## PolApproval_1 -1.31 0.30
## PolApproval_2 -1.35 0.24
## PolApproval_3 -0.74 0.22
## PolApproval_4 -0.90 0.22
## PolApproval_5 -0.82 0.25
## PolGov_1 -1.23 5.56
## PolEcon -1.37 0.40
## PolSoc -1.16 0.39## The following `from` values were not present in `x`: 5, 6## # A tibble: 4 x 3
## PolParty n percent
## <chr> <int> <chr>
## 1 Democrat 8 29.6%
## 2 Independent 4 14.8%
## 3 Liberal third party 1 3.7%
## 4 Republican 14 51.9%## vars n mean sd median trimmed mad min max range skew kurtosis se
## Relig1 1 27 4.67 2.35 5 4.78 2.97 1 7 6 -0.52 -1.33 0.45
## Relig2 2 27 4.48 2.58 5 4.48 2.97 1 8 7 -0.05 -1.52 0.50Checking Death and aging anxiety questions. Looking at min to max values they look okay.
## vars n mean sd median trimmed mad min max range skew kurtosis
## D_Anxiety_1 1 27 2.93 1.36 2 2.91 1.48 1 5 4 0.31 -1.38
## D_Anxiety_2 2 27 2.74 1.46 3 2.70 1.48 1 5 4 0.15 -1.43
## D_Anxiety_3 3 27 2.96 1.45 3 2.96 1.48 1 5 4 -0.08 -1.56
## D_Anxiety_4 4 27 2.56 1.55 2 2.48 1.48 1 5 4 0.43 -1.40
## D_Anxiety_5 5 27 3.33 1.39 4 3.39 1.48 1 5 4 -0.42 -1.20
## D_Anxiety_6 6 27 2.85 1.26 3 2.83 1.48 1 5 4 0.27 -0.94
## D_Anxiety_7 7 27 3.41 1.45 4 3.48 1.48 1 5 4 -0.41 -1.29
## D_Anxiety_8 8 27 2.70 1.44 2 2.65 1.48 1 5 4 0.28 -1.38
## D_Anxiety_9 9 27 3.56 1.31 3 3.65 1.48 1 5 4 -0.26 -1.15
## O_Worry 10 27 3.04 1.56 3 3.04 2.97 1 5 4 -0.06 -1.54
## D_Worry 11 27 3.07 1.54 3 3.09 2.97 1 5 4 -0.12 -1.49
## se
## D_Anxiety_1 0.26
## D_Anxiety_2 0.28
## D_Anxiety_3 0.28
## D_Anxiety_4 0.30
## D_Anxiety_5 0.27
## D_Anxiety_6 0.24
## D_Anxiety_7 0.28
## D_Anxiety_8 0.28
## D_Anxiety_9 0.25
## O_Worry 0.30
## D_Worry 0.30Checking Literacy, Numeracy, and MinMax questions. Looking at min to max values they look okay.
## vars n mean sd median trimmed mad min max range skew kurtosis
## Literacy 1 27 2.00 1.30 1 1.83 0.00 1 5 4 1.01 -0.26
## Num1 2 27 3.59 1.55 4 3.61 1.48 1 6 5 -0.23 -0.98
## Num2 3 27 4.37 1.64 5 4.52 1.48 1 6 5 -0.73 -0.62
## Num3 4 27 4.26 1.43 4 4.35 1.48 1 6 5 -0.29 -0.80
## Max_Min 5 27 3.59 1.74 4 3.61 2.97 1 6 5 -0.06 -1.39
## se
## Literacy 0.25
## Num1 0.30
## Num2 0.32
## Num3 0.28
## Max_Min 0.33Checking Debrief responses. They seem a nice bunch!
## [1] ""
## [2] "very interesting!!!"
## [3] ""
## [4] ""
## [5] ""
## [6] ""
## [7] ""
## [8] "Good smattering of questions."
## [9] "no thanks"
## [10] "none"
## [11] "None"
## [12] "None"
## [13] ""
## [14] ""
## [15] "Easy to understand very straight forward"
## [16] ""
## [17] ""
## [18] ""
## [19] ""
## [20] ""
## [21] ""
## [22] ""
## [23] ""
## [24] ""
## [25] ""
## [26] ""
## [27] ""