1.Explorary Data Anaylsis

1.1 Gender

  • Male students are larger than females

1.2 School

  • Proportion of participants in the school level

2 Question level analysis

Q3. How many jobs can you name (up to 10)? Please list them below.

Q3_1 Frequencies (how many jobs they can tell?)

* The number of answers are peaked at 10

Q3_2 Gender

## # A tibble: 2 × 5
##   gender number  mean median    sd
##   <fct>   <int> <dbl>  <dbl> <dbl>
## 1 Female    331  9.12     10  2.16
## 2 Male      320  8.53     10  2.75
  • Female students can tell more than males (averaged answers are 9.12 for female students and 8.53 for male students)

Q3_3 Age

- Age does not seem associated with the number of jobs they can write down /n * The number of answers might be associated with the age because older students are likely to speak more jobs. This box plot gives some ideas on the relationship between the number of jobs they can tell and their age. For instance, lines in each box do not vary over age.

Q3_4 School

- “Hope High School” show the lowest number that respondents can tell

Q3_5 The most frequent words

  • The most frequently-answered jobs are teacher, doctor and nurse and so on the size of font represent the frequency (the larger font means the greater frequency)

Q3_6 Gender difference

- The most frequent answer is the teacher, but girls tend to write down a teacher - Only girls report vet in the job, while boys mention footballer,mechanic and etc * this graph presents the top 15 jobs that student answered. red color means the females and Turkish color represents the male.

Q4. Can you name 10 jobs that didn’t exist 10 years ago? List as many as you can (up to 10) below.

Q4_1 Proportion of “I don`t know”

## # A tibble: 2 × 3
##   gender     n missing_rate
##   <fct>  <int>        <dbl>
## 1 Female   377        0.509
## 2 Male     399        0.531

Q4_2 Wordcloud

  • Uber driver, influencer, Tiktok marketer etc

Q4_3 Gender difference in word frequencies

Q5. What jobs do you think you would be good at?

Q5_1 Proportion of “I don`t know”

## # A tibble: 2 × 3
##   gender     n completion_rate
##   <fct>  <int>           <dbl>
## 1 Female   377           0.955
## 2 Male     399           0.942

Q5_2 Wordcloud

  • Traditional jobs are frequently responded (teacher, doctor, footballer, lawyer etc)
  • Some emerging jobs (Youtuber etc)

Q5_3 Gender difference

- Female tend to think teacher and lawyers are good jobs

Q9. How would you get a ‘green job’?

Q9_1 Proportion of “I don`t know”

## # A tibble: 2 × 3
##   gender     n missing_rate
##   <fct>  <int>        <dbl>
## 1 Female   377        0.239
## 2 Male     399        0.248

Q9_2 Wordcloud

  • Environment and green and, agriculture are key words

  • This wordcloud represents the most frequent words. I would use “stemming” method because it can capture the variation of words. For instance, Environmentally, Environment, Environmental -> “enviorn”

Q9_3 Gender difference?

Q8. What skills do you think you need for a ‘green job’?

Q8_1 Proportion of “I don`t know”

## # A tibble: 2 × 3
##   gender     n missing_rate
##   <fct>  <int>        <dbl>
## 1 Female   377        0.310
## 2 Male     399        0.298

Q8_2 wordcloud

  • environment, teamwork, care, math and nature are catching
  • stemming method is applied to make this wordcloud

Q8_3 gender difference

- Some nature oriented words - environment, nature, planting - Other basic skillset-related words - math, GCSE, English

Q9. How would you get a ‘green job’?

Q9_1 Proportion of “I don`t know”

## # A tibble: 2 × 3
##   gender     n missing_rate
##   <fct>  <int>        <dbl>
## 1 Female   377        0.459
## 2 Male     399        0.358

Q9_2 Wordcloud

  • Action-oriented words : applying (appli), interview, and qualified (qualif)
  • Nature-related words : environment, green, nature etc
  • Academic requirement : gcse, math, English, university etc

Q9_3 Gender difference

Q11. Do you know what a District Heating Network is?

Q11_1 Proportion of “I don`t know”

## # A tibble: 2 × 3
##   gender     n completion_rate
##   <fct>  <int>           <dbl>
## 1 Female   377           0.979
## 2 Male     399           0.972

Q11_2 Proportion of yes

## # A tibble: 3 × 2
##   district_heating     n
##   <chr>            <int>
## 1 No                 740
## 2 Yes                 68
## 3 <NA>                42

Q11_3 Proportion of yes according to the gender

## # A tibble: 4 × 3
##   district_heating gender     n
##   <chr>            <fct>  <int>
## 1 No               Female   345
## 2 No               Male     350
## 3 Yes              Female    24
## 4 Yes              Male      38

Q13. Describe what type of work you think you would like to be doing when you are 30

Q13_1 Proportion of “I don`t know”

## # A tibble: 2 × 3
##   gender     n missing_rate
##   <fct>  <int>        <dbl>
## 1 Female   377        0.188
## 2 Male     399        0.160

Q13_2 Wordcloud

  • The most frequent job they want in 30 is designer, engineer, businessman and so on.
  • As explained above, the words seem weird but it is because the similar words are put together. For instance, there are a variety variance of design including design, designer, designing. The stemming method put this word group together to streamline into “design”

Q13_3 Gender difference

Q19. How important to you is fighting climate change?

Q19_1 Proprtion

Q27 (20. Do you know what the ‘climate crisis’ is?)

Q27_1 Proportion of “I don`t know”

## # A tibble: 2 × 3
##   gender     n missing_rate
##   <fct>  <int>        <dbl>
## 1 Female   377       0.0451
## 2 Male     399       0.0226

Q27_2 Gender difference

## # A tibble: 2 × 3
##   gender     n awareness
##   <fct>  <int>     <dbl>
## 1 Female   360     0.561
## 2 Male     390     0.574

3.Summary statistic

3.1 Overall missing rate

3.2 Overall missing rate according to the gender