DATA 606 Data Project Proposal
Mark Schmalfeld
10/31/2021
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## completePart 1 - Introduction
The World Values Survey globally asks observational questions in 16 major categories plus demographics. This assessment focuses on use of the self assessment of Happiness and Well-being to evaluate correlations and significant differences in one areas: 1) The correlation of the self assessment of happiness question with the other questions in the section happiness and wellness section and 2) Additionally, inclusion of demographic factors is targeted such as age, religion, gender and in country groups based on the Inglehart-Welzel World Cultural Map to evaluate at least two different groups of countries related to the Inglehard_welzel cultural map.
Part 2 - Data
Research question
You should phrase your research question in a way that matches up with the scope of inference your dataset allows for. [ Each part needs to include an evaluation of the effect of Inglehard_welzel map on the predictive correlation for at least a couple of regions and created country groups]
Research Question) Are general health, life situation and financial siutation question predictive of an individuals self reported happiness?
For each of the above evaluate the predictive correlation with consideration groupings of countries based on the Inglehard-Welzel map for at least two groups and consider if time allows the additional effects of at least one of the following: gender, age, ethinicity and religious preference. (selected countries to downsize dataset based on Ingelhard-Welzel map but still have meaning full analysis set)
Cases
What are the cases, and how many are there?
Over 70,000 cases exist in the dataset (76,897 cases). A survey questionare based on the participants responses to question on culture preferences and demographics represents a case. Data is collected globally by survey and it is observational data. (self reported)
Cases are reduced considerable upon first sorting to remove those who did not respond to the question of interest. (reduced to 42,929 cases) and this removes all of the negative values from the responses which were used to capture those who did not respond to the question.
ALL data is categorical except Age is numeric. Both the target response value and the variables. They are tracked as numeric levels.
Data collection
Describe the method of data collection.
Data is collected by the World Cultural Values survey. It is conducted on a periodic basis (approx every 3 yr) and the survey is done globally to ensure as a diverse and complete a picture of the values held around the world.
The specific questions considered in this project are detailed below. The two main categories for the questions are “Happiness and Well-being section” and questions from the demographics.
Response
What is the response variable, and what type is it (numerical/categorical)?
Data Types for Response and Variables (Q46 is response all rest are variables)
Data Item Numeric or Categorical Type
Q46 -Q47 Categorical Qualitative Ordinal - Target Response Q48-Q50 Categorical Qualitative Interval
Q51-55 Categorical Qualitative Ordinal Q56 Categorical Qualitative Nominal
Q260 Categorical Qualitive Nominal Q262 Numeric Quantative Discrete Q273,Q275,Q276 Categorical Qualitative Nominal Q287 Categorical Qualitative Ordinal Q289, Q290 Categorical Qualitative Nominal
The response variable for part one is happiness (Q46).
Q46. Taking all things together, would you say you are (read out and code one answer): 1 Very happy 2 Rather happy 3 Not very happy 4 Not at all happy
Explanatory
What is the explanatory variable, and what type is it (numerical/categorical)? Details in table above.
The explanatory variable are questions score for Q47 to Q56 with groupings by specific culture country map and analysis to consider impacts of a demographic variable.
All data is categorical data in Questions Q47 to Q56. All demographics are categorical except Age is numeric with discrete interval.
Q47. All in all, how would you describe your state of health these days? Would you say it is… (read out): 1 Very good 2 Good 3 Fair 4 Poor 5 Very poor
(SHOW CARD 5) Please use this scale where 1 means “no choice at all” and 10 means “a great deal of choice” to indicate how much freedom of choice and control you feel you have over the way your life turns out (code one number): the 1-10 score is used for Q48- Q50.
Q48. Some people feel they have completely free choice and control over their lives, while other people feel that what they do has no real effect on what happens to them. (1 to 10) 1 no choice at all and 10 a great deal of choice
Q49. All things considered, how satisfied are you with your life as a whole these days? Using this card on which 1 means you are “completely dissatisfied” and 10 means you are “completely satisfied” where would you put your satisfaction with your life as a whole? (Code one number): Completely dissatisfied (1) to Completely satisfied (10) - scored from 1 to 10
Q50. How satisfied are you with the financial situation of your household? Please use this card again to help with your answer (code one number): Completely dissatisfied Completely satisfied 1 2 3 4 5 6 7 8 9 10
Q51 to Q55 are scored from 1 to 4. ( 4 best situation and 1 worse situation)
In the last 12 months, how often have your or your family…? Often Sometimes Rarely Never Q51 Gone without enough food to eat 1 2 3 4 Q52 Felt unsafe from crime in your home Q53 Gone without medicine or medical treatment that you needed Q54 Gone without a cash income Q55 Gone without a safe shelter over your head
Q56. Comparing your standard of living with your parents’ standard of living when they were about your age, would you say that you are better off, worse off or about the same? 1. Better off, 2. Worse off, 3. Or about the same.
Special coding if data fits one of these categories.The general coding for missing codes is as follows (do not read them and code only if the respondent mentions them: -1 Don’t know -3 Not applicable (filter) -2 No answer/refused -5 Missing; Not applicable for other reasons [ Most of this data in these categories seems grounds to filter out of the datasets and only evaluate data greater than zero]
Demographic data categories and scoring
All of the demographic data is categorical.
Q260. Respondent’s sex (Code respondent’s sex by observation, don’t ask about it!): Q261. only two scores 1 Male or 2 Female are possible
Q262. Age: This means you are _______ years old (write in age in two digits).
Q273
Are you currently (read out and code one answer only): Married 1 Living together as married 2 Divorced 3 Separated 4 Widowed 5 Single 6
Q275-278. What is the highest educational level that you, your spouse, your mother and your father have attained?3 [Interviewer: code for each person separately. The table below uses codes ISCED-2011 – International Standard Classification for Education used by the UN and UNESCO. Your supervisor will provide you with a national-adapted list of codes. If the respondent has no spouse, no father or no mother, code “-3”=not applicable Note, ‘completed’ = diploma or certificate] Code from 1 to 8 (low to higher level of education) 0 Early childhood education (ISCED 0) / no education 1 Primary education (ISCED 1) 2 Lower secondary education (ISCED 2) 3 Upper secondary education (ISCED 3) 4 Post-secondary non-tertiary education (ISCED 4) 5 Short-cycle tertiary education (ISCED 5) 6 Bachelor or equivalent (ISCED 6) 7 Master or equivalent (ISCED 7) 8 PHD Please, code the level of education according to the
Q287. People sometimes describe themselves as belonging to the working class, the middle class, or the upper or lower class. Would you describe yourself as belonging to the (read out and code one answer): 1 Upper class 2 Upper middle class 3 Lower middle class 4 Working class 5 Lower class (SHOW CARD 30)
Q289. Do you belong to a religion or religious denomination? If yes, which one? (Code answer due to list below. Code 0, if the respondent answers “ no denomination”) No: do not belong to a denomination 0 Yes: Roman Catholic 1 Protestant 2 Orthodox (Russian/Greek/etc.) 3 Jew 4 Muslim 5 Hindu 6 Buddhist 7 Other (write in):_____________ 8
Type of study
What type of study is this (observational/experiment)?
This is an observational study.
Note: WVS datasets are freely available for download and use. Permission is required to republish.
Part 3 - Exploratory data analysis
Data set downloaded on harddrive pending figuring out how to open a zip file from github. Data on Github as zip file.
Select specific columns of interest and filter out missing responses (all classified less than 0) to work with data where we only have responses.
Plot box plot for key variables and run summary statistics.
#URL="https://github.com/schmalmr/606_Fall_2021_Final-Project/blob/main/WVS_CrossNational_Wave_7.csv.zip"
#temp <- tempfile()
#download.file(URL,temp)
#df <- read.table(unz(temp, "WVS_CrossNational_Wave_7.csv.zip"))
#unlink(temp)
WVS_CrossNational_Wave_7<-read_csv("/Users/mark/Library/Mobile Documents/com~apple~CloudDocs/606 Data Probability Fall 2021/606 Project Final/WVS_CrossNational_Wave_7.csv")
df<-data.frame(WVS_CrossNational_Wave_7)
df<-select(df,B_COUNTRY,Q46,Q47,Q48,Q49,Q50,Q51,Q52,Q53,Q54,Q55,Q56,Q260,Q262,Q273,Q275,Q276,Q287,Q289)
df<- filter(df,Q46>0,Q47>0,Q48>0,Q49>0,Q50>0,Q51>0,Q52>0,Q53>0,Q54>0,Q55>0,Q56>0,Q56>0,Q260>0,Q262>0,Q273>0,Q275>0,Q276>0,Q289>=0,Q287>=0)
(summary(df))## B_COUNTRY Q46 Q47 Q48
## Min. : 20.0 Min. :1.000 Min. :1.000 Min. : 1.000
## 1st Qu.:158.0 1st Qu.:1.000 1st Qu.:2.000 1st Qu.: 6.000
## Median :398.0 Median :2.000 Median :2.000 Median : 8.000
## Mean :417.2 Mean :1.818 Mean :2.181 Mean : 7.245
## 3rd Qu.:642.0 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.: 9.000
## Max. :840.0 Max. :4.000 Max. :5.000 Max. :10.000
## Q49 Q50 Q51 Q52
## Min. : 1.000 Min. : 1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 6.000 1st Qu.: 5.000 1st Qu.:3.000 1st Qu.:3.000
## Median : 8.000 Median : 7.000 Median :4.000 Median :4.000
## Mean : 7.203 Mean : 6.282 Mean :3.544 Mean :3.452
## 3rd Qu.: 9.000 3rd Qu.: 8.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :10.000 Max. :10.000 Max. :4.000 Max. :4.000
## Q53 Q54 Q55 Q56
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:4.000 1st Qu.:1.000
## Median :4.000 Median :4.000 Median :4.000 Median :1.000
## Mean :3.392 Mean :3.227 Mean :3.748 Mean :1.685
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :3.000
## Q260 Q262 Q273 Q275 Q276
## Min. :1.000 Min. :16.0 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:34.0 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :44.0 Median :1.000 Median :3.000 Median :3.000
## Mean :1.525 Mean :45.7 Mean :1.306 Mean :3.606 Mean :3.491
## 3rd Qu.:2.000 3rd Qu.:56.0 3rd Qu.:1.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :2.000 Max. :99.0 Max. :6.000 Max. :8.000 Max. :8.000
## Q287 Q289
## Min. :1.000 Min. :0.000
## 1st Qu.:3.000 1st Qu.:1.000
## Median :3.000 Median :3.000
## Mean :3.234 Mean :3.071
## 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :5.000 Max. :9.000Plots to review data.
- A scatter plot prepared and of limited value since you do not see the differences in number of respondents for the different categories. A plot of age vs happiness shows the range of ages interviewed and that we have a range of answers across the entire range. Histogram provides better indication of number of respondents for each level of happiness.
- Histogram plots prepared to review the individual variables and the targeted response variable distributiions.
- Box plots really do not apply as the data is categorical.
- Summary statistics do provide good indications that variables do have different averages though data is not numeric for the most part but categorical so not most useful comparison.
- The histograms/ bar charts of the data give a better indication. The tendency in most responses seems to be pretty positive making me wonder if our data is really completely representative of the populations - those who would respond to a survey are likley to be more eductated or literate and maybe better off in general.
ggplot(df,aes(x=Q46,y=260))+geom_col ()+ggtitle("Happiness perception Bar chart * 1 very happy to 4 Not happy at all")
ggplot(df,aes(x=Q47, y=260))+geom_col ()+ggtitle("Barchart of your perception of Health * 1 very good to 5 very poor")
ggplot(df,aes(x=Q48,y=260))+geom_col()+ggtitle("Bar chart you level of free choice/control your life* 1 no choice to 10 complete control")
ggplot(df,aes(x=Q49,y=260))+geom_col()+ggtitle("Barchart of how satisfied you are with life * 1 dissatisfied to 10 dissatisfied")
ggplot(df,aes(x=Q50,y=260))+geom_col()+ggtitle("Barchart are you satified with your financial stability* 1 dissatisfied to 10 completely satisfied")
ggplot(df,aes(x=Q51,y=Q260))+geom_col()+ggtitle("Barchart Hunger* 1 often hungry to 4 never hungry")
ggplot(df,aes(x=Q52,y=Q260))+geom_col()+ggtitle("Barchart feeling unsafe from crime *1 often feel unsafe to 4 always feel safe ")
ggplot(df,aes(x=Q53,y=Q260))+geom_col()+ggtitle("Barchart going without medical treatment: 1 often to 4 never go without treatment")
ggplot(df,aes(x=Q54,y=Q260))+geom_col()+ggtitle("Barchart Lacking a cash income: * 1 often lack cash income to never lack cash income")
ggplot(df,aes(x=Q55,y=Q260))+geom_col()+ggtitle("Barchart- Being Homeless* 1 often to 4 never homeless")
ggplot(df,aes(x=Q56,y=Q260))+geom_col()+ggtitle("Bar chart of life compared to parents better (1), worse (2) or same (3)")
ggplot(df,aes(x=Q260,y=Q260))+geom_col()+ggtitle("Barchart Gender")
ggplot(df,aes(x=Q262,y=Q260))+geom_col()+ggtitle("Barchart Age of survey participants")
ggplot(df,aes(x=Q289,y=Q260))+geom_col()+ggtitle("Barchart religion categories")
ggplot(df,aes(x=Q273,y=Q260))+geom_col()+ggtitle("Barchart respondents martial status")
ggplot(df,aes(x=Q275,y=Q260))+geom_col()+ggtitle("Barchart respondents educational level")
ggplot(df,aes(x=Q276,y=Q260))+geom_col()+ggtitle("Barchar respondents spouse's educational level")
ggplot(df,aes(x=Q287,y=Q260))+geom_col()+ggtitle("Barchart Percieved social class level")
#Example Scatter plot
ggplot(data=df,aes(x=Q262,y=Q46,color=Q260))+geom_point()+ggtitle("Scatter plot Age vs Happiness with gender color overlay")
#Histograms
ggplot(df,aes(x=Q46))+geom_histogram (binwidth=0.5)+ggtitle("Happiness perception histogramv * 1 very happy to 4 Not happy at all")
ggplot(df,aes(x=Q47))+geom_histogram()+ggtitle("Histogram on your perception of Health * 1 very good to 5 very poor")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(df,aes(x=Q48))+geom_histogram()+ggtitle("Histogram on ability to have free choice/control your life* 1 no choice to 10 complete control")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(df,aes(x=Q49))+geom_histogram()+ggtitle("Histogram: are you satisfied with lifea* 1 dissatisfied to 10 completely satisfied")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(df,aes(x=Q50))+geom_histogram()+ggtitle("Histogram are you satified with your financial stability* 1 dissatisfied to 10 completely satisfied")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(df,aes(x=Q51))+geom_histogram(binwidth=1)+ggtitle("Histogram Hunger* 1 often hungry to 4 never hungry")
ggplot(df,aes(x=Q52))+geom_histogram(binwidth=1)+ggtitle("Histogram feeling unsafe from crime *1 often feel unsafe to 4 always feel safe ")
ggplot(df,aes(x=Q53))+geom_histogram(binwidth=1)+ggtitle("Histogram going without medical treatment: 1 often go without to 4 never go without treatment")
ggplot(df,aes(x=Q54))+geom_histogram(binwidth=1)+ggtitle("Histogram Lacking a cash income: * 1 often lack cash income to never lack cash income")
ggplot(df,aes(x=Q55))+geom_histogram(binwidth=1)+ggtitle("Histogram Being Homeless* 1 often homeless to 4 never homeless")
ggplot(df,aes(x=Q56))+geom_histogram(binwidth=1)+ggtitle("Histogram better (1), worse (2) or same (3) off as parents overall")
ggplot(df,aes(x=Q260))+geom_histogram(binwidth=1)+ggtitle("Histogram Gender")
ggplot(df,aes(x=Q262))+geom_histogram(binwidth=1)+ggtitle("Histogram Age of survey participants")
ggplot(df,aes(x=Q289))+geom_histogram(binwidth=1)+ggtitle("Histogram religion categories")
ggplot(df,aes(x=Q273))+geom_histogram(binwidth=1)+ggtitle("Histogram respondents martial status")
ggplot(df,aes(x=Q275))+geom_histogram(binwidth=1)+ggtitle("Histogram respondents educational level")
ggplot(df,aes(x=Q276))+geom_histogram(binwidth=1)+ggtitle("Histogram respondents spouse's educational level")
ggplot(df,aes(x=Q287))+geom_histogram(binwidth=1)+ggtitle("Histogram Percieved social class level")
#Example box plot
ggplot(df,aes(y=Q46, color=Q46))+geom_boxplot(width=0.5)+ggtitle("happiness boxplot")
ggplot(df,aes(x=Q47,y=Q46))+geom_boxplot(width=0.5)+ggtitle("health box plot")## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
ggplot(df,aes(x=Q48,y=Q46, color=Q289))+geom_boxplot(width=0.5)+ggtitle("Freechoice-control of own life boxplot")## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
ggplot(df,aes(x=Q49,y=Q46, color=Q46))+geom_boxplot(width=0.5)+ggtitle("overall life satisfaction boxplot")## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
ggplot(df,aes(x=Q50,y=Q46, color=Q275))+geom_boxplot(width=0.5)+ggtitle("Financial well being boxplot")## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
ggplot(df,aes(x=Q51,y=Q46, color=Q275))+geom_boxplot(width=0.5)+ggtitle("Hunger or not boxplot")## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
ggplot(df,aes(x=Q52,y=Q46, color=Q275))+geom_boxplot(width=0.5)+ggtitle(" Safety boxplot")## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
ggplot(df,aes(x=Q53,y=Q46, color=Q275))+geom_boxplot(width=0.5)+ggtitle("Gone without medical boxplot")## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
ggplot(df,aes(x=Q54,y=Q46, color=Q275))+geom_boxplot(width=0.5)+ggtitle(" Gone without case income boxplot")## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
ggplot(df,aes(x=Q55,y=Q46, color=Q275))+geom_boxplot(width=0.5)+ggtitle(" Homeless boxplot")## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
ggplot(df,aes(x=Q56,y=Q46, color=Q275))+geom_boxplot(width=0.5)+ggtitle(" Better, worse or same off as parents boxplot")## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
africa_target<-c(818,360,504,400,792,634,586,368,364,434,682)
df_africa_islam<-filter(df, B_COUNTRY %in% africa_target)
protestantEU_target<-c(752,208,578,352,246,528,276,756)
df_protestant_europe<-filter(df, B_COUNTRY %in% protestantEU_target)
confucian_target<-c(410,344,158,156,392)
df_confucian<-filter(df, B_COUNTRY %in% confucian_target)
orthodoxEU_target<-c(804,643,498,398,300,891,112,100,642,51)
df_orthodoxEU<-filter(df, B_COUNTRY %in% orthodoxEU_target)
SouthAsia_target<-c(702,458,152,764,704,376)
df_southwestasia<-filter(df, B_COUNTRY %in% SouthAsia_target)
Latinamerica_target<-c(484,608,862,170,630,32,604,68,76,332,218,858)
df_latinamerica<-filter(df,B_COUNTRY %in% Latinamerica_target)
English_target<-c(36,554,840,826)
df_english<-filter(df, B_COUNTRY %in% English_target)
CatholicEU_target<-c(380,203,40,250,348,616,620,724,191,56)
df_catholicEU<-filter(df, B_COUNTRY %in% CatholicEU_target)
summary_africa<-(summary(df_africa_islam))
summary_protestanteurope<-(summary(df_protestant_europe))
summary_confucian<-(summary(df_confucian))
summary_orthodox_eu<-(summary(df_orthodoxEU))
summary_sw_asia<-(summary(df_southwestasia))
summary_latinamerica<-(summary(df_latinamerica))
summaryenglish<-(summary(df_english))
(summary(df_catholicEU))## B_COUNTRY Q46 Q47 Q48 Q49
## Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA Median : NA Median : NA Median : NA
## Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA
## Q50 Q51 Q52 Q53 Q54
## Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA Median : NA Median : NA Median : NA
## Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA
## Q55 Q56 Q260 Q262 Q273
## Min. : NA Min. : NA Min. : NA Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA Median : NA Median : NA Median : NA
## Mean :NaN Mean :NaN Mean :NaN Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA Max. : NA Max. : NA Max. : NA
## Q275 Q276 Q287 Q289
## Min. : NA Min. : NA Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
## Median : NA Median : NA Median : NA Median : NA
## Mean :NaN Mean :NaN Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA Max. : NA Max. : NAggplot(df_africa_islam,aes(x=Q46,y=Q260))+geom_col ()+ggtitle("Africa_Islam Happiness perception Bar chart * 1 very happy to 4 Not Happy")
ggplot(df_catholicEU,aes(x=Q46,y=Q260))+geom_col ()+ggtitle("Catholic EU Happiness perception Bar chart * 1 very happy to 4 Not happy")
ggplot(df_confucian,aes(x=Q46,y=Q260))+geom_col ()+ggtitle("Confucian Happiness perception Bar chart * 1 very happy to 4 Not happy")
ggplot(df_english,aes(x=Q46,y=Q260))+geom_col ()+ggtitle("English speaking -Happiness perception Bar chart * 1 very happy to 4 Not happy")
ggplot(df_latinamerica,aes(x=Q46,y=Q260))+geom_col ()+ggtitle("Latin America Happiness perception Bar chart * 1 very happy to 4 Not happy")
ggplot(df_orthodoxEU,aes(x=Q46,y=Q260))+geom_col ()+ggtitle("Orthodox EU Happiness perception Bar chart * 1 very happy to 4 Not happy")
ggplot(df_protestant_europe,aes(x=Q46,y=Q260))+geom_col ()+ggtitle("Protestant EU Happiness perception Bar chart * 1 very happy to 4 Not happy")
ggplot(df_southwestasia,aes(x=Q46,y=Q260))+geom_col ()+ggtitle("South & West Asia Happiness perception Bar chart * 1 very happy to 4 Not happy")
Part 4 - Inference
The data set is large and the overall the categorical data set seems to have yielded little of great value when just looking at the histograms.
Histograms seem overwhelming skewed to the more positive category rating/ ranking in the responses. Wide age distribution, demographics distribution appears good overall.
There may be a risk we have missed in the survey more of those who are poor, are not literate or who would not have been likely to have taken/ been interviewed in one of these surveys.
Those that did not respond may be significant but beyond the fact they did not respond we do not know why or have an indication of what might have been the reason they did not respond.
Q46 on happiness perception seems to be centered around 2 which is a realatively good happiness indicator overall. The counts below 2 are significantly lower.
Data sorted into Ingelhart-Welzel Culture Map to allow further analysis. Initial bar chart and summary statistics to review if there is any difference in the avearge score and bar counts for happiness ( Q46) There does seem to be a small difference between regions and Latin America seems to have the highest proportion that is happy. Further reveiw required but summary statistics also indicate this different.
Part 5 - Conclusion
So far - there humans in this survey no matter what sex, demographic seem to feel pretty happy. Suggesting the starting Hypothesis that there is no overwhelming difference in happiness around the world as a good Null Hypothesis. The next step is to see if any of the variables have a strong correlation with happiness.
References
Haerpfer, C., Inglehart, R., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano J., M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2020. World Values Survey: Round Seven - Country-Pooled Datafile. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat. doi.org/10.14281/18241.13
Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014. World Values Survey: All Rounds - Country-Pooled Datafile Version: https://www.worldvaluessurvey.org/WVSDocumentationWVL.jsp. Madrid: JD Systems Institute.
The Inglehart-Welzel World Cultural Map - World Values Survey 7 (2020) [Provisional version]. Source: http://www.worldvaluessurvey.org/