course: Data Analysis in Sociology
author: Ekaterina Skriptsova
date: 2020-02-15
Do developers think that people born today will have a better life than their parents?
Well, this work examines exactly this question using Stack Overflow Developer Survey 2019, which covers various aspects of developers' life. In this report, I focus on 6 variables: Age, Gender, Region, SocialMedia, BlockchainIs, and BetterLife -- the outcome.
Age and Gender may seem as obvious ones, as representatives of different genders and generations sometimes have different attitudes to life.
Social media may be used to develop a sense of belonging and connectedness, which can improve wellbeing and perhaps make people more optimistic about the future of children. I expected that the belief in happy future will differ depending on the social media that is used the most due to different "nature" of the services — some of them are just messengers allowing people to chat with friends, some also give an opportunity to read news, while social networking sites do all of that — show news, happy friends, some with perfect lives. Some also are the so-called superapps, e.g. WeChat, that include a whole set of different features in one application. Unfortunately, in this dataset we do not know the original reason of why and for what people use the social media (news, chat, etc.), so let's just see whether it differs. In sum, the news feeds here are different, and maybe sometimes the less you know — the better?
Region -- originally created from the Country variable, was added because both countries and regions do differ economically.
BlockchainIs was the last variable added to the analysis, suggested by my colleague A. Nikulin. This is a developing technology, which probably may inspire the developers and make them believe in a brighter future. New tech - better life?
Does the belief about better life of youth differ depending on the selected predictors?
The outcome variable is BetterLife: "Will People Born Today Have a Better Life Than Their Parents?".
As for the predictors, the possible ones are:
Region(constructed fromCountry)GenderSocialMediaBlockchainIsAge
Final equation in general form: $$log(\frac{p(BetterLife_Yes)}{p(1 - BetterLife_Yes)}) = \beta_0 + \beta_1Region + \beta_2BlockchainIs + \beta_3Gender + \beta_4Age$$
df = res %>% dplyr::select(Country, SocialMedia, Gender, Age, BlockchainIs, BetterLife)
df = remove_attributes(df, "spec")
df = df %>% dplyr::filter(!is.na(BetterLife))
factor_vars = c("Country", "SocialMedia", "Gender", "BlockchainIs",
"BetterLife")
df[factor_vars] = data.frame(lapply(df[factor_vars], to_factor))
df$Region = countrycode::countrycode(df$Country, origin = "country.name", destination = "region")In this section, exploratory data analysis is performed in order to see how the selected variables are distributed, which should be further put into the model and may be important overall. Firstly, I make sure that variables of interest do not have too much missing data, and as a result - drop several ideas due to this fact. Then proceed with plotting the variables to check within which ones the percentage of those who believe that life for children will be better differs.
At the data loading stage, I've deleted observations that did not have label for the outcome variable (BetterLife). Now let's see whether other variables have missing values, and if their amount is > 15% - try to remove them. Otherwise - think what can be done about that.
As you can see from the following plot, BlockchainIs has ~30% of missings (similar to other predictors in full dataset), but without this one the models will be worse and less interesting, so I'll keep it. Uh.
Another possible predictor that has many missing values is Age. This variable plays an important role in deciding whether people born today will have a better life than parents, according to publications on the Internet [1]. In total, approximately 10% of 86269 values are missing here. Thus, let's keep the variable but drop guys with unknown age.
After removal of missing values in the previously mentioned variables, the dataset contains 50 841 observations. Now the data has the following variables, which were used to create several models:
| feature | description |
|---|---|
| BetterLife | Do you think people born today will have a better life than their parents? |
| Gender | Which of the following do you currently identify as? |
| Age | What is your age (in years)? |
| SocialMedia | What social media site do you use the most? |
| Region | In which region (orig. - country) do you currently reside? |
| BlockchainIs | Blockchain / cryptocurrency technology is primarily: |
And basic descriptive statistics:
## The data contains 50841 observations of the following variables:
## - SocialMedia: 14 levels: Facebook (n = 7402); Hello (n = 4); I don't use social media (n = 3099); Instagram (n = 3627); LinkedIn (n = 2456); Reddit (n = 10283); Snapchat (n = 416); Twitter (n = 7657); VK ВКонта́кте (n = 326); WeChat 微信 (n = 337); Weibo 新浪微博 (n = 32); WhatsApp (n = 7533); Youku Tudou 优酷 (n = 5) and YouTube (n = 7664)
## - Gender: 7 levels: Man (n = 47179); Man;Non-binary, genderqueer, or gender non-conforming (n = 117); Non-binary, genderqueer, or gender non-conforming (n = 330); Woman (n = 3055); Woman;Man (n = 42); Woman;Man;Non-binary, genderqueer, or gender non-conforming (n = 27) and Woman;Non-binary, genderqueer, or gender non-conforming (n = 91)
## - Age: Mean = 30.43, SD = 8.92, range = [1, 99], 0 missing
## - BlockchainIs: 5 levels: A passing fad (n = 8596); An irresponsible use of resources (n = 7822); Useful across many domains and could change many aspects of our lives (n = 14805); Useful for decentralized currency (i.e., Bitcoin) (n = 6086) and Useful for immutable record keeping outside of currency (n = 13532)
## - BetterLife: 2 levels: No (n = 18205) and Yes (n = 32636)
## - Region: 20 entries: Northern America, n = 15330; Western Europe, n = 7697; Northern Europe, n = 6314 and 17 others
Let's proceed & see how these are distributed.
As stated in the task, the target variable is BetterLife - "Will People Born Today Have a Better Life Than Their Parents?". The users are quite optimistic about that: 64% think that life will improve.
Maybe they are working on new innovative technologies that will make the world a better place? Who knows! 😎
In the full dataset, there are 7 unique options for Gender, however, in the Stack Overflow report they are split into:
(1) Man,
(2) Woman,
(3) Non-binary, genderqueer, or gender non-conforming.
Seems reasonable to join the levels into these three. The majority of respondents are male - approximately 93%; females constitute 6%, and non-binary, genderqueer, or gender non-conforming - 1%.
## BetterLife
## Gender No Yes
## Man 16504 30675
## Man;Non-binary, genderqueer, or gender non-conforming 52 65
## Non-binary, genderqueer, or gender non-conforming 169 161
## Woman 1406 1649
## Woman;Man 7 35
## Woman;Man;Non-binary, genderqueer, or gender non-conforming 12 15
## Woman;Non-binary, genderqueer, or gender non-conforming 55 36
The following figure shows that the highest percentage of those thinking that life will be better is among males (65%), followed by females (54%) which is 11% less compared to representatives of the opposite gender. The latter - Non-binary, genderqueer, or gender non-conforming (51.4%).
As mentioned in the introduction, people of different age groups have different opinion towards the life improvement. Does the relationship differ in Stack Overflow data?
First of all, Age variable contains several outliers: some respondents are just 1 year old & are already coding - definitely a mistake. Better to drop these guys and leave the developers aged from 14 to 80. Now there are 50 699 observations.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 50841 30.43 8.92 29 29.52 7.41 1 99 98 1.19 2.57 0.04
Caribbean,Central Asia,Middle Africa and Western Africa have relatively small number of observations in several cells.
Based on the map & Google, I united Middle Africa with Western Africa, and Central Asia - with Western Asia because sometimes it is represented as "Western & Central Asia". Melanesia was also united with Australia and New Zealand. Caribbean were left as is because it differs from the nearby regions and is located somewhere between.
## BetterLife
## Region No Yes
## Caribbean 20 80
## Central America 158 398
## Eastern Africa 63 233
## Eastern Asia 220 632
## Eastern Europe 1033 3070
## Melanesia & Australia and New Zealand 562 1002
## Middle & Western Africa 63 293
## Northern Africa 72 219
## Northern America 5865 9406
## Northern Europe 2477 3818
## South America 522 1569
## South-Eastern Asia 199 733
## Southern Africa 151 275
## Southern Asia 1533 3755
## Southern Europe 1200 1955
## Western & Central Asia 471 965
## Western Europe 3558 4129
The most optimistic people are in Middle & Western Africa (82%), Caribbean (80%), Eastern Africa (78.7%) and South-Eastern Asia (78.5%). The least - among the developers in Southern (62%), Northern (61.6%) and Western Europe (53.7%). Wow. In comparison, 74.8% of those in Eastern Europe, including Russia, believe that youth will have a better life than their parents. Developers in Northern America (e.g. USA) are also not sure that life will improve: only 61.6% of them agreed.
SocialMedia shows the respondent's most frequently used social media. The most popular here are: Facebook, WhatsApp, Twitter, Reddit, YouTube; 3082 users do not use social media at all.
Overall, Facebook & VK are social networking sites, that have both quite rich newsfeeds and communication tools. LinkedIn - job-related stuff. Reddit, Snapchat, Instagram, Youtube & Youku Tudou can be grouped into entertainment services, however, some of them also may act as a place for discussions (1st one), and the format slightly differs - the latter two are video-hosting services. WhatsApp - a messenger owned by Facebook; WeChat is a Chinese super-app, which includes a lot of features from messaging, social media to mobile payment app. Twitter - mostly about microblogging (and posting sad stuff), Weibo - also microblogging website but popular in China.
Hello is also a real social network, however, only 4 people frequently use it. Maybe they are the developers of this site? Spoiler: this level was not significant, but should we keep it or drop it? Joining it with Weibo and Youku Tudou did not make much difference, so it's better to delete these several observations.
## BetterLife
## SocialMedia No Yes
## Facebook 2533 4862
## Hello 0 4
## I don't use social media 1457 1625
## Instagram 1249 2365
## LinkedIn 896 1558
## Reddit 3580 6682
## Snapchat 125 288
## Twitter 3080 4571
## VK ВКонта́кте 56 268
## WeChat 微信 71 263
## Weibo 新浪微博 6 26
## WhatsApp 2714 4813
## Youku Tudou 优酷 1 3
## YouTube 2399 5204
The highest percentage of those thinking that people born today will have a better life than their parents is among users of VK 💙 - 82.7% selected "Yes", which is quite understandable given that most of them come from Eastern Europe (e.g. Russia), where situation was not the best one and now has improved. This is followed by the users of WeChat: 78.7% of them are sure. This application is widely popular in China, so this result may be related to the country's growth [2].
Those who frequently use worldwide SNs and entertainment services, such as Snapchat, YouTube, Instagram, Facebook and Reddit - have from 69 to 63% of believers in better life. In particular, for FB users figure is 65.75%, which is by ~17% lower compared to VK. Perhaps, this is due to Facebook being used by people all around the world, and the attitude may differ depending on the country.
Twitter, as I expected, has one of the lowest percentages - only 59.74% of those who use it frequently have optimistic thoughts about the future. And approximately half of those who do not use social media have a similar attitude - 52.73%. I suppose that this may happen due to several reasons: maybe some users do not browse social media because they are disappointed with what they saw at some point, or just think that social media makes life sadder, or social media may make someone feel rather isolated and not more connected, etc.
In sum, those who frequently use worldwide entertainment services, e.g. Youtube or Snapchat, have higher percantage of those thinking that youth will live better than their parents, while those who use Twitter (news + tweets about life) and LinkedIn (job-related) - are least optimistic about the future, followed by those who do not use social media at all.
And a bonus/additional variable suggested in a chat with A. Nikulin: BlockchainIs (Blockchain / cryptocurrency technology is primarily:..).
One may think how can this one be related to the BetterLife? Well, turned out that BlockchainIs is connected to the outcome. The variable has 5 levels:
- A passing fad (n = 8569);
- An irresponsible use of resources (n = 7794);
- Useful across many domains and could change many aspects of our lives (n = 14751);
- Useful for decentralized currency (i.e., Bitcoin) (n = 6035);
- Useful for immutable record keeping outside of currency (n = 13510)
Developers who consider blockchain as useful in some way are positively thinking about the youth having a better life - from 71.3% to 64.5%! Those who have rather negative attitude (a passing fad or an irresponsible use of resources) have lower value.
## The data contains 50659 observations of the following variables:
## - SocialMedia: 11 levels: Facebook (n = 7395); I don't use social media (n = 3082); Instagram (n = 3614); LinkedIn (n = 2454); Reddit (n = 10262); Snapchat (n = 413); Twitter (n = 7651); VK ВКонта́кте (n = 324); WeChat 微信 (n = 334); WhatsApp (n = 7527) and YouTube (n = 7603)
## - Gender: 3 levels: Man (n = 47019); Non-binary, genderqueer, or gender non-conforming (n = 600) and Woman (n = 3040)
## - Age: Mean = 30.46, SD = 8.81, range = [14, 79], 0 missing
## - BlockchainIs: 5 levels: A passing fad (n = 8569); An irresponsible use of resources (n = 7794); Useful across many domains and could change many aspects of our lives (n = 14751); Useful for decentralized currency (i.e., Bitcoin) (n = 6035) and Useful for immutable record keeping outside of currency (n = 13510)
## - BetterLife: 2 levels: No (n = 18160) and Yes (n = 32499)
## - Region: 17 levels: Northern America (n = 15266); Caribbean (n = 100); Central America (n = 556); Eastern Africa (n = 296); Eastern Asia (n = 823); Eastern Europe (n = 4103); Melanesia & Australia and New Zealand (n = 1563); Middle & Western Africa (n = 355); Northern Africa (n = 291); Northern Europe (n = 6294); South America (n = 2091); South-Eastern Asia (n = 929); Southern Africa (n = 426); Southern Asia (n = 5288); Southern Europe (n = 3155); Western & Central Asia (n = 1436) and Western Europe (n = 7687)
logit_sm = readRDS("logit_sm.rds")
logit_age = readRDS("logit_age.rds")
logit_reg = readRDS("logit_reg.rds")
logit_block = readRDS("logit_block.rds")The first models contain only one predictor. Among the presented, the best one, according to the R2 Tjur, AIC, and log-Likelihood is the one with Region, where the reference level is Northern America. Second-best - BlockchainIs. Let's proceed with more complex models and see what happens.
| BetterLife | BetterLife | BetterLife | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | std. Error | CI | p | Odds Ratios | std. Error | CI | p | Odds Ratios | std. Error | CI | p |
| (Intercept) | 1.92 | 0.02 | 1.83 – 2.01 | <0.001 | 1.60 | 0.02 | 1.55 – 1.66 | <0.001 | 1.51 | 0.02 | 1.44 – 1.57 | <0.001 |
| Reference | Reference | Reference | ||||||||||
| I don't use social media | 0.58 | 0.04 | 0.53 – 0.63 | <0.001 | ||||||||
| 0.99 | 0.04 | 0.91 – 1.07 | 0.750 | |||||||||
| 0.91 | 0.05 | 0.82 – 1.00 | 0.042 | |||||||||
| 0.97 | 0.03 | 0.91 – 1.04 | 0.383 | |||||||||
| Snapchat | 1.20 | 0.11 | 0.97 – 1.49 | 0.097 | ||||||||
| 0.77 | 0.03 | 0.72 – 0.83 | <0.001 | |||||||||
| VK ВКонта́кте | 2.49 | 0.15 | 1.88 – 3.37 | <0.001 | ||||||||
| WeChat 微信 | 1.93 | 0.14 | 1.49 – 2.54 | <0.001 | ||||||||
| 0.92 | 0.03 | 0.86 – 0.99 | 0.021 | |||||||||
| YouTube | 1.13 | 0.03 | 1.06 – 1.21 | <0.001 | ||||||||
| Northern America | Reference | Reference | Reference | |||||||||
| Caribbean | 2.50 | 0.25 | 1.56 – 4.19 | <0.001 | ||||||||
| Central America | 1.57 | 0.10 | 1.31 – 1.90 | <0.001 | ||||||||
| Eastern Africa | 2.31 | 0.14 | 1.76 – 3.08 | <0.001 | ||||||||
| Eastern Asia | 1.78 | 0.08 | 1.52 – 2.09 | <0.001 | ||||||||
| Eastern Europe | 1.85 | 0.04 | 1.72 – 2.00 | <0.001 | ||||||||
| Melanesia & Australia and New Zealand |
1.11 | 0.06 | 1.00 – 1.24 | 0.050 | ||||||||
| Middle & Western Africa | 2.89 | 0.14 | 2.21 – 3.84 | <0.001 | ||||||||
| Northern Africa | 1.90 | 0.14 | 1.46 – 2.50 | <0.001 | ||||||||
| Northern Europe | 0.96 | 0.03 | 0.91 – 1.02 | 0.199 | ||||||||
| South America | 1.88 | 0.05 | 1.69 – 2.08 | <0.001 | ||||||||
| South-Eastern Asia | 2.29 | 0.08 | 1.95 – 2.69 | <0.001 | ||||||||
| Southern Africa | 1.14 | 0.10 | 0.93 – 1.39 | 0.214 | ||||||||
| Southern Asia | 1.53 | 0.03 | 1.43 – 1.64 | <0.001 | ||||||||
| Southern Europe | 1.02 | 0.04 | 0.94 – 1.10 | 0.686 | ||||||||
| Western & Central Asia | 1.28 | 0.06 | 1.14 – 1.43 | <0.001 | ||||||||
| Western Europe | 0.72 | 0.03 | 0.68 – 0.77 | <0.001 | ||||||||
| A passing fad | Reference | Reference | Reference | |||||||||
| An irresponsible use of resources |
0.69 | 0.03 | 0.65 – 0.73 | <0.001 | ||||||||
| Useful across many domains and could change many aspects of our lives |
1.65 | 0.03 | 1.56 – 1.74 | <0.001 | ||||||||
| Useful for decentralized currency (i.e., Bitcoin) |
1.47 | 0.04 | 1.37 – 1.57 | <0.001 | ||||||||
| Useful for immutable record keeping outside of currency |
1.21 | 0.03 | 1.14 – 1.28 | <0.001 | ||||||||
| Observations | 50659 | 50659 | 50659 | |||||||||
| R2 Tjur | 0.008 | 0.022 | 0.021 | |||||||||
| AIC | 65731.651 | 65009.810 | 65091.232 | |||||||||
| log-Likelihood | -32854.825 | -32487.905 | -32540.616 | |||||||||
Model comparison using anova shows that model with Region is significantly better than model with BlockchainIs, while model with blockchain is not better than social media, and social media - not significantly better than age.
## Analysis of Deviance Table
##
## Model 1: BetterLife ~ Age
## Model 2: BetterLife ~ SocialMedia
## Model 3: BetterLife ~ BlockchainIs
## Model 4: BetterLife ~ Region
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 50657 65275
## 2 50648 65710 9 -434.56
## 3 50654 65081 -6 628.42
## 4 50642 64976 12 105.42 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The results obtained using one predictor may be quite different from those received using multiple predictors, thus, I built several models with multiple predictors to see whether including them will make the final model better, and to decide whether I should include both SocialMedia and Region as they may be related.
The predictors include: Region, SocialMedia, Gender, Age, BlockchainIs.
Based on the anova output, two models (with 4 predictors each) look better than the other ones. However, they do not differ significantly.
## Analysis of Deviance Table
##
## Model 1: BetterLife ~ SocialMedia + Gender + Age
## Model 2: BetterLife ~ Region + Gender + Age
## Model 3: BetterLife ~ SocialMedia + Region + Gender + Age
## Model 4: BetterLife ~ BlockchainIs + Region + Gender + Age
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 50645 64788
## 2 50639 64140 6 647.85 < 2.2e-16 ***
## 3 50629 63973 10 167.35 < 2.2e-16 ***
## 4 50635 63553 -6 419.82
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The likelihood ratio test shows the best (closest to 0) value is achieved by the last model, which included BlockchainIs, Region, Gender, Age. It would also be nice to look at the model with interactions between SocialMedia and Region, however, there would be too many coefficients (& NAs) to interpret, so let's skip that. As SocialMedia and Region may be related, I'll proceed with model 4 - BetterLife ~ BlockchainIs + Region + Gender + Age.
## Likelihood ratio test
##
## Model 1: BetterLife ~ SocialMedia + Gender + Age
## Model 2: BetterLife ~ Region + Gender + Age
## Model 3: BetterLife ~ SocialMedia + Region + Gender + Age
## Model 4: BetterLife ~ BlockchainIs + Region + Gender + Age
## #Df LogLik Df Chisq Pr(>Chisq)
## 1 14 -32394
## 2 20 -32070 6 647.85 < 2.2e-16 ***
## 3 30 -31987 10 167.35 < 2.2e-16 ***
## 4 24 -31777 -6 419.82 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Other metrics are summarized in the following outputs.
The first llh column shows the log-likelihood for the fitted models, and by comparing it to the llhNull intercept-model, all models are better than null model. The highest McFadden pseudo r-squared is achieved by model with all predictors (3.87%), followed by the one with Region, SocialMedia, Gender and Age - 3.24%. Other measures also show that the last two models are better than the others.
## llh llhNull G2 McFadden r2ML
## -3.239416e+04 -3.305682e+04 1.325309e+03 2.004592e-02 2.582213e-02
## r2CU
## 3.542874e-02
## llh llhNull G2 McFadden r2ML
## -3.207024e+04 -3.305682e+04 1.973158e+03 2.984495e-02 3.820102e-02
## r2CU
## 5.241295e-02
## llh llhNull G2 McFadden r2ML
## -3.198656e+04 -3.305682e+04 2.140505e+03 3.237615e-02 4.137298e-02
## r2CU
## 5.676497e-02
## llh llhNull G2 McFadden r2ML
## -3.177665e+04 -3.305682e+04 2.560326e+03 3.872614e-02 4.928448e-02
## r2CU
## 6.761979e-02
## Rank Df.res AIC AICc BIC McFadden Cox.and.Snell Nagelkerke p.value
## 1 14 50640 64820 64820 64950 0.02005 0.02582 0.03543 9.326e-276
## 2 20 50640 64180 64180 64370 0.02984 0.03820 0.05241 0.000e+00
## 3 30 50630 64040 64040 64310 0.03238 0.04137 0.05676 0.000e+00
## 4 24 50640 63600 63600 63820 0.03873 0.04928 0.06762 0.000e+00
The model output shows that all final predictors are significant, there is no need to drop any.
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: BetterLife
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 50658 66114
## BlockchainIs 4 1032.40 50654 65081 < 2.2e-16 ***
## Region 16 803.72 50638 64278 < 2.2e-16 ***
## Gender 2 164.30 50636 64113 < 2.2e-16 ***
## Age 1 559.91 50635 63553 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The Hosmer-Lemeshow Test is used to find evidence of poor fit. The p-value here is > 0.05, meaning that there is no evidence that the selected model is bad, nice!
##
## Hosmer and Lemeshow test (binary model)
##
## data: df$BetterLife, fitted(logit_AgeGenRegBlock)
## X-squared = 19.842, df = 24, p-value = 0.7057
Let's go deeper and use cross-validation to fit the best model, finally examine the coefficients, after that - look at the performance metrics.
Here the data is split into train (80%) and test (20%); cross validation: 5-fold, metric to select the optimal model -- ROC.
X = df
set.seed(17)
split = createDataPartition(X$BetterLife, p=0.8, list = FALSE)
X_train = X[split, ]
X_valid = X[-split, ]The output below shows the raw coefficients and the respective confidence intervals. The model equation is:
$$log(\frac{p(BetterLife_Yes)}{p(1 - BetterLife_Yes)}) = 1.26 + 0.65Region_Caribbean + 0.29Region_Central_America + \0.48Region_Eastern_Africa + 0.46Region_Eastern_Asia + \0.48Region_Eastern_Europe + 0.14Region_Melanesia_Australia_And_New_Zealand + \0.62Region_Middle_And_Western_Africa + 0.39Region_Nothern_Africa - \0.04Region_Northern_Europe + 0.45Region_South_America + \0.65Region_South_Eastern_Asia - 0.03Region_Southern_Africa + \0.1Region_Southern_Asia - 0.12Region_Southern_Europe + 0.07Region_Western_Central_Asia - \0.4Region_Western_Europe - 0.3Blockchain_Is_Anirresponsible_Use_Of_Resources + \0.39Blockchain_Is_Useful_Across_Many_Domains_And_Could_Change_Many_Aspects_Of_Our_Lives + \0.22Blockchain_Is_Useful_For_Decentralized_Currency + \0.18Blockchain_Is_Useful_For_Immutable_Record_Keeping_Outside_Of_Currency - \0.45Gender_NonBinary_Genderqueer_Or_Gender_NonConforming - \0.5Gender_Woman - 0.03*Age$$
| .outcome | |||
|---|---|---|---|
| Predictors | Log-Odds | CI | p |
| (Intercept) | 1.26 | 1.17 – 1.36 | <0.001 |
| RegionCaribbean | 0.65 | 0.12 – 1.24 | 0.022 |
| RegionCentral America | 0.29 | 0.07 – 0.50 | 0.010 |
| RegionEastern Africa | 0.48 | 0.17 – 0.79 | 0.003 |
| RegionEastern Asia | 0.46 | 0.28 – 0.65 | <0.001 |
| RegionEastern Europe | 0.48 | 0.39 – 0.57 | <0.001 |
| RegionMelanesia & Australia and New Zealand |
0.14 | 0.02 – 0.27 | 0.025 |
| RegionMiddle & Western Africa |
0.62 | 0.33 – 0.93 | <0.001 |
| RegionNorthern Africa | 0.39 | 0.10 – 0.70 | 0.011 |
| RegionNorthern Europe | -0.04 | -0.11 – 0.03 | 0.276 |
| RegionSouth America | 0.45 | 0.33 – 0.57 | <0.001 |
| RegionSouth-Eastern Asia | 0.65 | 0.47 – 0.83 | <0.001 |
| RegionSouthern Africa | -0.03 | -0.25 – 0.20 | 0.817 |
| RegionSouthern Asia | 0.10 | 0.02 – 0.18 | 0.017 |
| RegionSouthern Europe | -0.12 | -0.21 – -0.03 | 0.011 |
| RegionWestern & Central Asia |
0.07 | -0.06 – 0.20 | 0.290 |
| RegionWestern Europe | -0.40 | -0.46 – -0.33 | <0.001 |
| BlockchainIsAn irresponsible use of resources |
-0.30 | -0.37 – -0.23 | <0.001 |
| BlockchainIsUseful across many domains and could change many aspects of our lives |
0.39 | 0.33 – 0.46 | <0.001 |
| BlockchainIsUseful for decentralized currency (i.e., Bitcoin) |
0.22 | 0.14 – 0.30 | <0.001 |
| BlockchainIsUseful for immutable record keeping outside of currency |
0.18 | 0.12 – 0.24 | <0.001 |
| GenderNon-binary, genderqueer, or gender non-conforming |
-0.45 | -0.64 – -0.27 | <0.001 |
| GenderWoman | -0.50 | -0.58 – -0.41 | <0.001 |
| Age | -0.03 | -0.03 – -0.02 | <0.001 |
| Observations | 40528 | ||
| R2 Tjur | 0.050 | ||
Now, table shows the odds and confidence intervals for the final model.
| OR | 2.5 % | 97.5 % | |
|---|---|---|---|
| (Intercept) | 3.5348173 | 3.2075017 | 3.8964751 |
| RegionCaribbean | 1.9169631 | 1.1272932 | 3.4566079 |
RegionCentral America |
1.3299153 | 1.0751122 | 1.6552968 |
RegionEastern Africa |
1.6082431 | 1.1903216 | 2.2102080 |
RegionEastern Asia |
1.5889651 | 1.3281241 | 1.9100725 |
RegionEastern Europe |
1.6168138 | 1.4817926 | 1.7654813 |
RegionMelanesia & Australia and New Zealand |
1.1523770 | 1.0182183 | 1.3057894 |
RegionMiddle & Western Africa |
1.8601818 | 1.3874075 | 2.5382697 |
RegionNorthern Africa |
1.4804500 | 1.1009642 | 2.0208588 |
RegionNorthern Europe |
0.9625680 | 0.8987819 | 1.0310131 |
RegionSouth America |
1.5645756 | 1.3899310 | 1.7643605 |
RegionSouth-Eastern Asia |
1.9094037 | 1.5965758 | 2.2962740 |
RegionSouthern Africa |
0.9737107 | 0.7787505 | 1.2225291 |
RegionSouthern Asia |
1.1010281 | 1.0173118 | 1.1920596 |
RegionSouthern Europe |
0.8899249 | 0.8134082 | 0.9739942 |
RegionWestern & Central Asia |
1.0731250 | 0.9423467 | 1.2238861 |
RegionWestern Europe |
0.6729670 | 0.6313961 | 0.7172657 |
BlockchainIsAn irresponsible use of resources |
0.7405123 | 0.6898891 | 0.7948075 |
BlockchainIsUseful across many domains and could change many aspects of our lives |
1.4777415 | 1.3849138 | 1.5767700 |
BlockchainIsUseful for decentralized currency (i.e., Bitcoin) |
1.2509784 | 1.1552887 | 1.3548357 |
BlockchainIsUseful for immutable record keeping outside of currency |
1.1955734 | 1.1219330 | 1.2740050 |
GenderNon-binary, genderqueer, or gender non-conforming |
0.6365716 | 0.5298527 | 0.7652612 |
| GenderWoman | 0.6081737 | 0.5585500 | 0.6622957 |
| Age | 0.9742796 | 0.9719264 | 0.9766346 |
Interpretation of several coefficients:
- Intercept -- male developer in Northern America, believing that blockchain is a passing fad
- Being in Caribbean region increases the odds by a factor of 1.92 compared to dev. in Nothern America. Other regions also seem to be positive thinking
- Odds for developers in Southern Europe are by 11% lower compared to Northern America
- Being a female or non-binary decreases the odds by the factor of 0.61 and 0.64 respectively compared to male developers
- One unit increase in age is associated with 3% decrease in odds of positive belief
- Developers who consider blockchain useful across many domains and could change many aspects of our lives have odds higher by 48% compared to those believing that blockchain is a passing fad; An irresponsible use of resources - lower by 26%
To begin with, let's look at the confusion matrix for the test data. Overall, the model fit is statistically significantly better than no model at all (Null Information Rate).
Accuracy is 65.35%, which is slightly better than the original 64% of our positive class. Recall of 0.92 means that the model was able to identify 92% of those who believe in better life of children, and precision -- 66% of those identified as "Yes" actually responded "Yes".
## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 635 513
## Yes 2997 5986
##
## Accuracy : 0.6535
## 95% CI : (0.6442, 0.6628)
## No Information Rate : 0.6415
## P-Value [Acc > NIR] : 0.005826
##
## Kappa : 0.1129
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Precision : 0.6664
## Recall : 0.9211
## F1 : 0.7733
## Prevalence : 0.6415
## Detection Rate : 0.5909
## Detection Prevalence : 0.8867
## Balanced Accuracy : 0.5479
##
## 'Positive' Class : Yes
##
Higher the AUC, the better the model is at predicting the classes. Perfect classifier has an AUC of 1, while 0.5 indicates no class separation capacity. In this case it equals 62.97%, which is quite ok.
## Area under the curve: 0.6297
Marginal effects show how a changes in the independent variable impact the outcome variable, taking all other independent variables into account.
Interpreting everything probably does not make much sense, so let's selected several ones. For example,
- increase of
Ageby one year would decrease the p(y=1) by approximately -0.0057 - for two hypothetical male developers from Northern America, the predicted probability of optimistic thoughts about better life of children is 0.07 lower for a user who considers blockchain rather an irresponsible use of resources compared to one having "a passing fad" attitude to blockchain
## factor
## Age
## BlockchainIsAn irresponsible use of resources
## BlockchainIsUseful across many domains and could change many aspects of our lives
## BlockchainIsUseful for decentralized currency (i.e., Bitcoin)
## BlockchainIsUseful for immutable record keeping outside of currency
## GenderNon-binary, genderqueer, or gender non-conforming
## GenderWoman
## RegionCaribbean
## RegionCentral America
## RegionEastern Africa
## RegionEastern Asia
## RegionEastern Europe
## RegionMelanesia & Australia and New Zealand
## RegionMiddle & Western Africa
## RegionNorthern Africa
## RegionNorthern Europe
## RegionSouth America
## RegionSouth-Eastern Asia
## RegionSouthern Africa
## RegionSouthern Asia
## RegionSouthern Europe
## RegionWestern & Central Asia
## RegionWestern Europe
## AME SE z p lower upper
## -0.0057 0.0002 -24.0455 0.0000 -0.0061 -0.0052
## -0.0700 0.0076 -9.2172 0.0000 -0.0849 -0.0552
## 0.0881 0.0065 13.5283 0.0000 0.0754 0.1009
## 0.0547 0.0081 6.7561 0.0000 0.0388 0.0706
## 0.0409 0.0066 6.2458 0.0000 0.0281 0.0538
## -0.1218 0.0201 -6.0740 0.0000 -0.1611 -0.0825
## -0.1146 0.0091 -12.5786 0.0000 -0.1325 -0.0968
## 0.1356 0.0433 3.1286 0.0018 0.0506 0.2205
## 0.0547 0.0201 2.7258 0.0064 0.0154 0.0940
## 0.1032 0.0270 3.8212 0.0001 0.0503 0.1561
## 0.0888 0.0160 5.5457 0.0000 0.0574 0.1202
## 0.0996 0.0079 12.6064 0.0000 0.0841 0.1151
## 0.0232 0.0122 1.8952 0.0581 -0.0008 0.0472
## 0.1412 0.0238 5.9217 0.0000 0.0945 0.1880
## 0.0683 0.0280 2.4416 0.0146 0.0135 0.1231
## -0.0101 0.0071 -1.4251 0.1541 -0.0239 0.0038
## 0.0871 0.0107 8.1465 0.0000 0.0661 0.1080
## 0.1258 0.0148 8.5204 0.0000 0.0969 0.1548
## 0.0028 0.0232 0.1194 0.9050 -0.0428 0.0483
## 0.0191 0.0080 2.3856 0.0170 0.0034 0.0347
## -0.0243 0.0094 -2.5919 0.0095 -0.0427 -0.0059
## 0.0182 0.0131 1.3901 0.1645 -0.0074 0.0438
## -0.0893 0.0068 -13.1500 0.0000 -0.1026 -0.0760
The values are plotted below. Those crossing the line are not significant.
Let's visualize marginal effect for each level of BlockchainIs in combination with Age (as it also did not cause several warning messages to appear :)) )
As you can see, the overall relationship is similar for all levels of BlockchainIs: the probability decreases with Age. The highest probabilities are among those who consider blockchain as useful across many domains, followed by another two groups of developers who think that this technology is good for decentralized currency and immutable record keeping outside of currency. The lowest probabilities occur among those who think that blockchain is an irresponsible use of resources, and this is far below the reference level -- a passing fad.
A brief check whether the model is ok or something should be changed.
Overall, almost all observations fall into 2 standard-error bounds, which is good!
The next plot shows the residuals plotted against individual predictors, i.e. show whether there is some non-linearity of the logit over the covariates. Some suspicious activity is present in Region, others look somewhat okay. Also, Age has a significant statistic, meaning that model should include its quadratic term.
## Test stat Pr(>|Test stat|)
## BlockchainIs
## Region
## Gender
## Age 31.393 2.107e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Let's test. Well, it did not change the metrics, so I guess there is no need to dive deeper into manipulations with age.
## Area under the curve: 0.6297
No GVIF higher than 10, meaning that there is no multicollinearity.
## GVIF Df GVIF^(1/(2*Df))
## BlockchainIs 1.097852 4 1.011738
## Region 1.144075 16 1.004215
## Gender 1.016291 2 1.004048
## Age 1.072891 1 1.035805
The outlier test shows that there is only one observation that may be considered as outlier. Luckily, not significant. Observations 6487 and 37945 also have the largest Cook's distances, according to the plot, so they may be influential.
## No Studentized residuals with Bonferroni p < 0.05
## Largest |rstudent|:
## rstudent unadjusted p-value Bonferroni p
## 4713 -1.992909 0.046271 NA
All the bad guys:
## StudRes Hat CookD
## 4448 -1.991659 0.002390724 0.0006218848
## 4713 -1.992909 0.002350924 0.0006133267
## 6487 -1.941347 0.008385106 0.0019304206
## 11956 -1.545731 0.015109600 0.0014577058
## 37945 -1.975414 0.007946093 0.0019757676
## 43163 1.062364 0.015858794 0.0005100992
To check the effect of the outlier, I fit a model without these observations. No coefficients have changed significantly, so everything is okay. No need to change anything.
## Calls:
## 1: glm(formula = BetterLife ~ BlockchainIs + Region + Gender + Age, family =
## "binomial", data = df)
## 2: glm(formula = BetterLife ~ BlockchainIs + Region + Gender + Age, family =
## "binomial", data = df, subset = c(-4448, -4713, -6487, -11956, -37945,
## -43163))
##
## Model 1
## (Intercept) 1.2570
## SE 0.0444
##
## BlockchainIsAn irresponsible use of resources -0.2971
## SE 0.0323
##
## BlockchainIsUseful across many domains and could change many aspects of our lives 0.4037
## SE 0.0296
##
## BlockchainIsUseful for decentralized currency (i.e., Bitcoin) 0.2449
## SE 0.0365
##
## BlockchainIsUseful for immutable record keeping outside of currency 0.182
## SE 0.029
##
## RegionCaribbean 0.681
## SE 0.253
##
## RegionCentral America 0.2535
## SE 0.0967
##
## RegionEastern Africa 0.500
## SE 0.144
##
## RegionEastern Asia 0.4240
## SE 0.0822
##
## RegionEastern Europe 0.4807
## SE 0.0404
##
## RegionMelanesia & Australia and New Zealand 0.1052
## SE 0.0562
##
## RegionMiddle & Western Africa 0.714
## SE 0.141
##
## RegionNorthern Africa 0.320
## SE 0.138
##
## RegionNorthern Europe -0.0447
## SE 0.0313
##
## RegionSouth America 0.4153
## SE 0.0541
##
## RegionSouth-Eastern Asia 0.6246
## SE 0.0827
##
## RegionSouthern Africa 0.0124
## SE 0.1042
##
## RegionSouthern Asia 0.0862
## SE 0.0363
##
## RegionSouthern Europe -0.107
## SE 0.041
##
## RegionWestern & Central Asia 0.0820
## SE 0.0596
##
## RegionWestern Europe -0.384
## SE 0.029
##
## GenderNon-binary, genderqueer, or gender non-conforming -0.5315
## SE 0.0847
##
## GenderWoman -0.5013
## SE 0.0387
##
## Age -0.0260
## SE 0.0011
##
## Model 2
## (Intercept) 1.2581
## SE 0.0444
##
## BlockchainIsAn irresponsible use of resources -0.2973
## SE 0.0323
##
## BlockchainIsUseful across many domains and could change many aspects of our lives 0.4042
## SE 0.0296
##
## BlockchainIsUseful for decentralized currency (i.e., Bitcoin) 0.2451
## SE 0.0365
##
## BlockchainIsUseful for immutable record keeping outside of currency 0.182
## SE 0.029
##
## RegionCaribbean 0.817
## SE 0.270
##
## RegionCentral America 0.2533
## SE 0.0967
##
## RegionEastern Africa 0.500
## SE 0.144
##
## RegionEastern Asia 0.4238
## SE 0.0822
##
## RegionEastern Europe 0.4806
## SE 0.0404
##
## RegionMelanesia & Australia and New Zealand 0.1052
## SE 0.0562
##
## RegionMiddle & Western Africa 0.748
## SE 0.143
##
## RegionNorthern Africa 0.320
## SE 0.138
##
## RegionNorthern Europe -0.0447
## SE 0.0313
##
## RegionSouth America 0.4151
## SE 0.0541
##
## RegionSouth-Eastern Asia 0.6243
## SE 0.0827
##
## RegionSouthern Africa 0.0123
## SE 0.1042
##
## RegionSouthern Asia 0.0859
## SE 0.0363
##
## RegionSouthern Europe -0.107
## SE 0.041
##
## RegionWestern & Central Asia 0.0819
## SE 0.0596
##
## RegionWestern Europe -0.384
## SE 0.029
##
## GenderNon-binary, genderqueer, or gender non-conforming -0.5267
## SE 0.0848
##
## GenderWoman -0.5017
## SE 0.0387
##
## Age -0.0260
## SE 0.0011
##
This work examined the relationship between belief in whether youth will have better life than their parents and several variables. The final model included Region, BlockchainIs, Gender and Age. All of them were significant.
Overall, developers who consider blockchain useful have more optimistic thoughts compared to those with negative attitude. Region matters as well: most regions had higher odds of belief in better life compared to developers in Northern America. As for gender and age: females and non-binary are more suspicious about the future, as well as people with higher age.