# A tibble: 35 × 60
   Timestamp           Email …¹ Teacher   Age Gender Gadgets Years…² SC1.1 SC1.2
   <dttm>              <chr>    <chr>   <dbl> <chr>  <chr>   <chr>   <chr> <chr>
 1 2022-11-15 20:57:38 Respond… Faculty    30 Female Mobile… 9 mont… Stro… Agree
 2 2022-11-16 16:02:54 Respond… Faculty    32 Female Mobile… 1       Stro… Stro…
 3 2022-11-17 09:55:23 Respond… Faculty    33 Female Mobile… 11      Stro… Stro…
 4 2022-11-17 10:59:32 Respond… Faculty    36 Male   Mobile… 7 years Stro… Agree
 5 2022-11-17 11:04:48 Respond… Faculty    27 Female Mobile… 1 year  Stro… Agree
 6 2022-11-17 14:24:48 Respond… Faculty    30 Male   Mobile… 5 years Stro… Stro…
 7 2022-11-19 20:59:37 Respond… Faculty    38 Female Mobile… 6       Stro… Stro…
 8 2022-11-20 08:20:36 Respond… Faculty    31 Female Mobile… 9       Agree Agree
 9 2022-11-21 00:14:23 Respond… Faculty    34 Female Mobile… 10      Stro… Stro…
10 2022-11-22 15:10:45 Respond… Faculty    31 Female Mobile… 10      Stro… Stro…
# … with 25 more rows, 51 more variables: SC1.3 <chr>, SC1.4 <chr>,
#   SC1.5 <chr>, SC2.1 <chr>, SC2.2 <chr>, SC2.3 <chr>, SC2.4 <chr>,
#   SC2.5 <chr>, SC2.6 <chr>, SC2.7 <chr>, SC3.1 <chr>, SC3.2 <chr>,
#   SC3.3 <chr>, SC3.4 <chr>, SC3.5 <chr>, SC4.1 <chr>, SC4.2 <chr>,
#   SC4.3 <chr>, SC4.4 <chr>, FRSC1.1 <chr>, FRSC1.2 <chr>, FRSC1.3 <chr>,
#   FRSC1.4 <chr>, FRSC1.5 <chr>, FRSC1.6 <chr>, FRSC1.7 <chr>, FRSC1.8 <chr>,
#   FRSC1.9 <chr>, FRSC2.1 <chr>, FRSC2.2 <chr>, FRSC2.3 <chr>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

Socio-Demographic Profile

#Gender


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

# A tibble: 2 × 3
  Gender count Percentage
  <chr>  <int>      <dbl>
1 Female    26       74.3
2 Male       9       25.7

Age

Only with age responses

Mean Age for Complete Data

# A tibble: 1 × 2
  `Mean Age` `SD of Age`
       <dbl>       <dbl>
1       34.2        5.02

Gadgets for Complete Data

# A tibble: 6 × 3
  Gadgets                                      count Percentage
  <chr>                                        <int>      <dbl>
1 Computer                                         1       2.94
2 Mobile Phone, iPad/ Tablet                       1       2.94
3 Mobile Phone, iPad/ Tablet, Laptop               9      26.5 
4 Mobile Phone, iPad/ Tablet, Laptop, Computer     6      17.6 
5 Mobile Phone, Laptop                            16      47.1 
6 Mobile Phone, Laptop, Computer                   1       2.94

Years of Clinical Experience of the old data

# A tibble: 20 × 3
   `Years of Clinical Experience` count Percentage
   <chr>                          <int>      <dbl>
 1 <1 year                            1       2.86
 2 1                                  1       2.86
 3 1 year                             2       5.71
 4 10                                 5      14.3 
 5 11                                 1       2.86
 6 15                                 2       5.71
 7 16 years                           1       2.86
 8 2                                  2       5.71
 9 2.5 years                          2       5.71
10 3                                  3       8.57
11 4                                  1       2.86
12 5 years                            1       2.86
13 6                                  3       8.57
14 7 years                            2       5.71
15 8                                  1       2.86
16 8 mos                              1       2.86
17 9                                  1       2.86
18 9 months                           2       5.71
19 more than 10 years                 1       2.86
20 <NA>                               2       5.71

# A tibble: 35 × 61
   Timestamp           Email …¹ Teacher   Age Gender Gadgets Years…² SC1.1 SC1.2
   <dttm>              <chr>    <chr>   <dbl> <chr>  <chr>   <chr>   <chr> <chr>
 1 2022-11-15 20:57:38 Respond… Faculty    30 Female Mobile… 9 mont… Stro… Agree
 2 2022-11-16 16:02:54 Respond… Faculty    32 Female Mobile… 1       Stro… Stro…
 3 2022-11-17 09:55:23 Respond… Faculty    33 Female Mobile… 11      Stro… Stro…
 4 2022-11-17 10:59:32 Respond… Faculty    36 Male   Mobile… 7 years Stro… Agree
 5 2022-11-17 11:04:48 Respond… Faculty    27 Female Mobile… 1 year  Stro… Agree
 6 2022-11-17 14:24:48 Respond… Faculty    30 Male   Mobile… 5 years Stro… Stro…
 7 2022-11-19 20:59:37 Respond… Faculty    38 Female Mobile… 6       Stro… Stro…
 8 2022-11-20 08:20:36 Respond… Faculty    31 Female Mobile… 9       Agree Agree
 9 2022-11-21 00:14:23 Respond… Faculty    34 Female Mobile… 10      Stro… Stro…
10 2022-11-22 15:10:45 Respond… Faculty    31 Female Mobile… 10      Stro… Stro…
# … with 25 more rows, 52 more variables: SC1.3 <chr>, SC1.4 <chr>,
#   SC1.5 <chr>, SC2.1 <chr>, SC2.2 <chr>, SC2.3 <chr>, SC2.4 <chr>,
#   SC2.5 <chr>, SC2.6 <chr>, SC2.7 <chr>, SC3.1 <chr>, SC3.2 <chr>,
#   SC3.3 <chr>, SC3.4 <chr>, SC3.5 <chr>, SC4.1 <chr>, SC4.2 <chr>,
#   SC4.3 <chr>, SC4.4 <chr>, FRSC1.1 <chr>, FRSC1.2 <chr>, FRSC1.3 <chr>,
#   FRSC1.4 <chr>, FRSC1.5 <chr>, FRSC1.6 <chr>, FRSC1.7 <chr>, FRSC1.8 <chr>,
#   FRSC1.9 <chr>, FRSC2.1 <chr>, FRSC2.2 <chr>, FRSC2.3 <chr>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

Years of Clinical Experience of Cleaned Data

# A tibble: 3 × 3
  Years              count Percentage
  <chr>              <int>      <dbl>
1 5 to 10 years         17       51.5
2 at most 4 years       11       33.3
3 more than 10 years     5       15.2

SCALE 1

# A tibble: 35 × 113
   Timestamp           Email …¹ Teacher   Age Gender Gadgets Years…² SC1.1 SC1.2
   <dttm>              <chr>    <chr>   <dbl> <chr>  <chr>   <chr>   <chr> <chr>
 1 2022-11-15 20:57:38 Respond… Faculty    30 Female Mobile… 9 mont… Stro… Agree
 2 2022-11-16 16:02:54 Respond… Faculty    32 Female Mobile… 1       Stro… Stro…
 3 2022-11-17 09:55:23 Respond… Faculty    33 Female Mobile… 11      Stro… Stro…
 4 2022-11-17 10:59:32 Respond… Faculty    36 Male   Mobile… 7 years Stro… Agree
 5 2022-11-17 11:04:48 Respond… Faculty    27 Female Mobile… 1 year  Stro… Agree
 6 2022-11-17 14:24:48 Respond… Faculty    30 Male   Mobile… 5 years Stro… Stro…
 7 2022-11-19 20:59:37 Respond… Faculty    38 Female Mobile… 6       Stro… Stro…
 8 2022-11-20 08:20:36 Respond… Faculty    31 Female Mobile… 9       Agree Agree
 9 2022-11-21 00:14:23 Respond… Faculty    34 Female Mobile… 10      Stro… Stro…
10 2022-11-22 15:10:45 Respond… Faculty    31 Female Mobile… 10      Stro… Stro…
# … with 25 more rows, 104 more variables: SC1.3 <chr>, SC1.4 <chr>,
#   SC1.5 <chr>, SC2.1 <chr>, SC2.2 <chr>, SC2.3 <chr>, SC2.4 <chr>,
#   SC2.5 <chr>, SC2.6 <chr>, SC2.7 <chr>, SC3.1 <chr>, SC3.2 <chr>,
#   SC3.3 <chr>, SC3.4 <chr>, SC3.5 <chr>, SC4.1 <chr>, SC4.2 <chr>,
#   SC4.3 <chr>, SC4.4 <chr>, FRSC1.1 <chr>, FRSC1.2 <chr>, FRSC1.3 <chr>,
#   FRSC1.4 <chr>, FRSC1.5 <chr>, FRSC1.6 <chr>, FRSC1.7 <chr>, FRSC1.8 <chr>,
#   FRSC1.9 <chr>, FRSC2.1 <chr>, FRSC2.2 <chr>, FRSC2.3 <chr>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

Summary Statistics for Digital Competence and Faculty Readiness

Mean

  Group.1   DGSC11   DGSC12   DGSC13 DGSC14 DGSC15   DGSC21 DGSC22   DGSC23
1 Faculty 3.542857 3.457143 3.371429    3.2    3.4 3.628571    3.8 3.657143
    DGSC24   DGSC25 DGSC26   DGSC27   DGSC31   DGSC32   DGSC33   DGSC34
1 3.657143 3.657143    3.6 3.857143 3.514286 3.142857 3.714286 3.771429
    DGSC35   DGSC41   DGSC42   DGSC43   DGSC44   FRSC11   FRSC12   FRSC13
1 3.628571 3.714286 3.485714 3.514286 3.028571 3.514286 3.685714 3.514286
    FRSC14   FRSC15   FRSC16   FRSC17   FRSC18 FRSC19 FRSC21 FRSC22 FRSC23
1 3.485714 3.314286 3.628571 3.742857 3.771429    3.8    3.8    3.6    3.6
    FRSC24 FRSC25 FRSC26   FRSC27   FRSC28   FRSC29  FRSC210   FRSC31   FRSC32
1 3.457143    3.4    3.2 3.457143 3.485714 3.485714 3.485714 3.657143 3.628571
  FRSC33   FRSC34   FRSC35   FRSC36   FRSC41   FRSC42   FRSC43   FRSC44
1    3.6 3.314286 3.485714 3.428571 3.742857 3.371429 3.457143 3.685714
    FRSC45   FRSC46   FRSC47
1 2.971429 3.485714 3.371429

Standard Deviation

  Group.1    DGSC11    DGSC12    DGSC13    DGSC14    DGSC15    DGSC21    DGSC22
1 Faculty 0.8168396 0.6572159 0.8773528 0.7970534 0.6507914 0.4902409 0.4058397
     DGSC23    DGSC24    DGSC25    DGSC26    DGSC27    DGSC31    DGSC32
1 0.6390644 0.6390644 0.5392182 0.6039088 0.3550358 0.6584933 0.9121035
    DGSC33   DGSC34    DGSC35    DGSC41    DGSC42    DGSC43    DGSC44    FRSC11
1 0.518563 0.426043 0.4902409 0.4583492 0.5621089 0.5621089 0.8570028 0.7017385
     FRSC12    FRSC13    FRSC14    FRSC15   FRSC16    FRSC17    FRSC18
1 0.5826627 0.6584933 0.7017385 0.7959984 0.598317 0.5054327 0.4902409
     FRSC19    FRSC21    FRSC22    FRSC23    FRSC24    FRSC25    FRSC26
1 0.4058397 0.4058397 0.6039088 0.6039088 0.6108267 0.6507914 0.7194769
     FRSC27    FRSC28    FRSC29   FRSC210    FRSC31    FRSC32    FRSC33
1 0.6108267 0.5070926 0.6122009 0.6122009 0.5392182 0.5469549 0.4970501
     FRSC34    FRSC35    FRSC36    FRSC41    FRSC42    FRSC43    FRSC44
1 0.6761234 0.5621089 0.5576059 0.5054327 0.7310635 0.6572159 0.5297851
     FRSC45    FRSC46    FRSC47
1 0.7853704 0.6122009 0.6896595

Montera’s Group (Thesis)

Montera, et.al.

2022-12-03

Socio-Demographic Profile

Age

Only with age responses

Mean Age for Complete Data

Gadgets for Complete Data

Years of Clinical Experience of the old data

Years of Clinical Experience of Cleaned Data

SCALE 1

Summary Statistics for Digital Competence and Faculty Readiness

Mean

Standard Deviation