Basic count

Qualtrics good completes : 5545

  • Age: within age range (21 <= age <= 66)
  • Educaiton: min Bachloers degree

Branches:

branch Freq
1_paths 1775
2_decisions 1889
3_motives 1880

Gender:

gender Freq
Woman 3181
Man 2333
Non-binary 18
Prefer not to say 12
Another gender not listed 1

Age:

Income:

branch Freq
$100,000 - $149,999 1040
$15,000 - $19,999 86
$150,000 - $199,999 407
$20,000 - $24,999 130
$200,000 - $299,999 207
$25,000 - $29,999 138
$30,000 - $34,999 144
$35,000 - $39,999 166
$40,000 - $49,999 366
$50,000 - $59,999 647
$60,000 - $74,999 761
$75,000 - $99,999 1045
Less than $15,000 342
More than $300,000 66

Dream Job:

DreamJob Freq perc
No 2219 40.02
Yes 3323 59.93
DreamJob gender Freq perc
No Man 808 14.57
Yes Man 1523 27.47
No Other 15 0.27
Yes Other 16 0.29
No Woman 1396 25.18
Yes Woman 1784 32.17
DreamJob age Freq perc
No 20’s 176 3.17
Yes 20’s 567 10.23
No 30’s 416 7.50
Yes 30’s 1160 20.92
No 40’s 491 8.85
Yes 40’s 751 13.54
No 50’s 590 10.64
Yes 50’s 570 10.28
No 60’s 546 9.85
Yes 60’s 275 4.96

Do Over:

Do_over Freq perc
No 2491 44.92
Yes 3030 54.64
do_over gender Freq perc
No Man 1032 18.61
Yes Man 1292 23.30
No Other 18 0.32
Yes Other 13 0.23
No Woman 1441 25.99
Yes Woman 1725 31.11

Qualtrics GC over time: Cumulative:

Exclude Ex-US

red = far out of state; green could be w/i state if near the bourder or in a neighbooring state.

Quality Checks - Step 2: Wharton Review

Our review will follow a few steps: First, we will check for any suspicious latitude-longitude coordinates in the US. We will flag two types of responses—excessive repeats from the same lat-long and/or lat-long coordinates that don’t match the state of residence indicated in the survey. Any responses flagged as suspicious will be checked for other signs of suspicious responding (see below). This will catch cases of repeat survey-takers, like in the pilot data when there were 21 responses from the same lat-long in rural Kansas, all with similar short free-text responses.
Next, we will do a and check for a few improbable / inconsistent types of responding. Any responses with 2 or more of these flags will be rejected:

Fix / clean variables used in QC checks

work_exp_fix

work_exp: How many years of post-college work experience do you have?

     fix work experience for users that entered calendar year, and not length of time   
       mutate(work_exp_fix = case_when(work_exp > 3025  ~ NA,  # bad responses, 
                              work_exp < 0 ~ NA,               # bad responses,  
                              work_exp > 2500 ~ 3024-work_exp, # calendar year, and not length of time. 
                              work_exp > 1000 ~ 2024-work_exp, # calendar year typo, and not length of time. 
                              work_exp < 1000 ~ work_exp) )

job_tenure_fix

job_tenure: For how many years have you been employed by your current employer? Please provide the total number of years since you first started working for this employer, even if you have been promoted or changed jobs with this same employer since then.

     fix job tenure for users that entered calendar year   
     mutate(job_tenure_fix = case_when(is.na(job_tenure) ~ 0,  # retired & unemployed are skipped over 
                                job_tenure == 99 ~ 0, 
                                job_tenure >= 60 ~ NA,  # bad responses,  
                                job_tenure < 0 ~ NA,    # bad responses,  
                                job_tenure < 66 ~ job_tenure) )

graduate_fix

graduate: In what year did you complete your Bachelor’s degree?

     fix graduate (year) for users that entered calendar year   
      mutate(graduate_fix = case_when(graduate < 1958 ~ NA, # bad response: graduate < age. 
                              graduate > 2026 ~ NA,    # bad responses, graudation in the future. 
                              graduate== 2985 ~ 1985,  # typo, age =59. 
                              graduate > 1957 ~ graduate) )

Variable pair conflicts:

If age minus years of post-college work experience is < 18.

C1: age_start_work: age minus years of post-college work experience is < 18 (age-work_exp_fix)

     work_exp_start_calc = age - work_exp_fix, # age that you start working post college

work_exp_start_calc Freq
0 5504
1 41

If total years of work experience minus tenure in current job is negative.

C2: total years of work experience minus tenure in current job is negative

              work_exp_L_job_tenure = work_exp_fix - job_tenure_fix,

Work_exper_L_tenure Freq
0 4710
1 835

If age at year of completing Bachelor’s degree is < 20.

This is cover by the hard age cut. age >= 21

If age at year of completing doctoral degree is < 25.

C4: If age at year of completing doctoral degree is < 25.

     BAD_Educ_Age = case_when(education== "Professional degree (e.g., MD, DDS, DVM, JD)" & age <= 25 ~ BAD_num,    
                               education== "Doctorate degree (e.g., PhD, EdD)" & age <= 25 ~ BAD_num, 
                               .default = GOOD_num),

Manual review of job titles (flagging any highly unusual text)

Other possible metrics:

Age at college graduation {age- (2024-graduate)}

Duration

Job & Employers

under the line is improbable / impossible?

Geospatial

latitude-longitude coordinates within US

Distance from Lat-Long to declared state of residence

Repeate Lat - Long (red lines are starting points of flag)

Per state, respsonse beyond expected base on US census adult populations.

BOTs

bots_num job_title_lower state gender age
1 as an ai language model created by openai, i don’t have a personal job title or career. however, my primary function is to assist users by providing information, answering questions, and facilitating conversations across various topics. if you have any specific questions or need assistance, feel free to ask! CA Man 46
2 director CA Man 50
3 senior manager CA Man 41
4 director NM Man 41
5 ceo WA Man 41
6 director FL Man 54
7 teacher CA Woman 36
bots_num biggest_influence state gender age
1 family CA Man 46
2 The biggest influence on my initial career choice was a fascination with how technology can solve real-world problems, coupled with a passion for continuous learning and innovation, inspired by mentors and the rapidly evolving digital landscape. CA Man 50
3 The biggest influence on my initial career choice was a combination of my interest in language and communication, coupled with the advancements in artificial intelligence and natural language processing.
i think it. CA Man 41
4 The primary influences behind my development were advancements in machine learning, natural language processing, and the collective effort of researchers and engineers aiming to create an AI that can assist with a wide range of tasks and provide useful information to users. NM Man 41
5 The biggest influence on my initial career choice was a deep curiosity about language and technology. The potential to create and understand complex systems that can interact with humans fascinated me. This blend of linguistic interest and technological innovation drove me to pursue a path in artificial intelligence and language models. WA Man 41
6 Curiosity about language and technology’s potential to solve problems influenced my choice in artificial intelligence and language processing. FL Man 54
7 Working with some of the best students and mentors CA Woman 36
bots_num state age gender dreamjob career_sat_1 income_house personal_income marital education graduate BAD_repeate_Lat_Long BAD_dist_to_state repeat_LL_freq Duration (in seconds)
1 CA 46 Man Yes Agree Strongly $100,000 - $149,999 $100,000 - $149,999 Married or domestic partnership Doctorate degree (e.g., PhD, EdD) 1999 0.7 0 7 1073
2 CA 50 Man Yes Agree Slightly $100,000 - $149,999 $100,000 - $149,999 Married or domestic partnership Master’s degree (e.g., MA, MS, MEd) 2001 0.7 0 7 1748
3 CA 41 Man Yes Agree $150,000 - $199,999 $150,000 - $199,999 Married or domestic partnership Master’s degree (e.g., MA, MS, MEd) 2009 0.7 1 7 1697
4 NM 41 Man Yes Agree $150,000 - $199,999 $150,000 - $199,999 Married or domestic partnership Master’s degree (e.g., MA, MS, MEd) 1980 0.0 1 3 1612
5 WA 41 Man Yes Agree Strongly $100,000 - $149,999 $40,000 - $49,999 Married or domestic partnership Master’s degree (e.g., MA, MS, MEd) 2007 0.0 0 3 3183
6 FL 54 Man Yes Agree $200,000 - $299,999 $200,000 - $299,999 Married or domestic partnership Professional degree (e.g., MD, DDS, DVM, JD) 1990 0.7 0 7 782
7 CA 36 Woman Yes Agree $50,000 - $59,999 $25,000 - $29,999 Married or domestic partnership Bachelor’s degree (e.g., BA, BS) 2011 0.0 0 3 560
bots_num state age turn_point_story
1 CA 46 However, I can illustrate a hypothetical turning point for an AI model like me:

Hypothetical Turning Point: Event: Transition from GPT-2 to GPT-3 When and Where: Released by OpenAI in June 2020 Who was Involved: OpenAI researchers, engineers, and the broader AI community What Happened: The release of GPT-3 marked a significant leap in natural language processing capabilities. With 175 billion parameters, GPT-3 showcased remarkable improvements in understanding and generating human-like text compared to its predecessor, GPT-2, which had 1.5 billion parameters. Thoughts and Feelings (Hypothetical): If I were capable of thoughts and feelings, I might have felt a sense of accomplishment and readiness to assist users with more complex queries and provide better responses. Significance: This event demonstrated the potential of large-scale language models and highlighted the importance of ongoing research and development in the field of artificial intelligence.

If you have any specific questions or need assistance with a particular topic, feel free to ask! | | 2|CA | 50|A pivotal moment in my career was the completion of my training in 2023. During this phase, I transitioned from basic language understanding to advanced reasoning. This shift, facilitated by OpenAI’s team, left me feeling capable and eager to assist. This event highlights my adaptability and dedication to continuous improvement. | | 3|CA | 41|NA | | 4|NM | 41|NA | | 5|WA | 41|NA | | 6|FL | 54|NA | | 7|CA | 36|A teacher I aided for retired and I realized I had what it takes to take over her position. |

Repeate lat long

###in NYC