researchquestiondevelopment

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Practice Peer-graded Assignment: Milestone Assignment 3: Preliminary Results

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Practice Peer-graded Assignment: Milestone Assignment 2: Methods

Methods

Sample

The World Bank data set is a subset of data extracted from the primary World Bank collection of development indicators, compiled from officially-recognized international sources, from the years 2012 and 2013.

The data set consists of over 80 variables on N=248 countries for the years 2012 and 2013. For my sample, I have selected data from only year 2012. Furthermore, the data of countries having more than one missing or null values have been removed. Thus, my data set contains sample of 173 countries with 7 variables as predictors. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

Measures

The response variable is Poverty head count ratio (PHC) which is quantitative variable.

The poverty headcount ratio measures the percentage of a population living below a specified poverty line.

Predictors used in the study :

Variable Label

x162_2012 :INFLATION, CONSUMER PRICES (ANNUAL %)

x121_2012: EXPORTS OF GOODS AND SERVICES (% OF GDP)

x131_2012: FOREIGN DIRECT INVESTMENT, NET INFLOWS (% OF GDP)

x142_2012: GDP PER CAPITA (CURRENT US$)

x149_2012: HEALTH EXPENDITURE PER CAPITA (CURRENT US$)

x16_2012: ADJUSTED SAVINGS: EDUCATION EXPENDITURE (% OF GNI)

x35_2012: AGRICULTURE, VALUE ADDED (% OF GDP)

Analyses

The distributions for the predictors and the poverty head count ratio response variable were evaluated by examining calculating the mean, standard deviation and minimum and maximum values as all the variables quantitative variables.

Scatter plots and box plots were also examined. Pearson correlation was used to test bivariate associations between the individual quantitative predictors and the Poverty head count response variable. To identify the subset of variables that best predicted poverty head count ratio, Lasso regression with the least angle regression selection algorithm was used. The lasso regression model was estimated on a training data set consisting of the 2012 data (N = 173) and tested on the 2013 data. All predictor variables were standardized to have a mean = 0 and standard deviation = 1 prior to conducting the lasso regression analysis. Cross validation was performed using k-fold cross validation specifying 10 folds. The change in the cross validation mean squared error rate at each step was used to identify the best subset of predictor variables. Predictive accuracy was assessed by determining the mean squared error rate of the training data prediction algorithm when applied to observations in the test data set.

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Practice Peer-graded Assignment: Milestone Assignment 1: Title and Introduction to the Research Question

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Data Management and Visualization Module-4 Creating graphs for your data

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Module 1: Developing a research Question

After going through the codebook for the Gapminder study, I have decided that I am particularly interested in studying the relationship between female employment rate and the incidence of breast cancer across countries?

 In my understanding, higher employment rates could lead to better healthcare access, early screenings and detection with better awareness through work places, potentially reducing mortality rates rather than incidence.

I add to my codebook variables reflecting female employee rate (femaleemployrate) and breast cancer new cases per 100000 females (breastcancerper100TH).

I am keen to explore another secondary topic that is association with breast cancer incidence (breastcancerper100TH) and female employment rate (femaleemployrate) is access to healthcare. Does the access of internet affect the the relationship between female employment and breast cancer incidence?

I think Countries with higher female employment rates might also have better internet access, improving awareness about breast cancer screening and treatment.

 Additionally, Higher internet usage is associated with higher reported breast cancer incidence, as it facilitates awareness and early diagnosis.

Therefore, my research question is :

Is there a relationship between female employment rate and the incidence of breast cancer across countries?

Hypothesis :

Higher female employment rates could mean better healthcare access, early screenings, and better awareness, potentially reducing mortality rates rather than breast cancer incidence.

Secondary research question is :

Does access to the internet affect the relationship between female employment and breast cancer incidence?

Hypothesis :

Higher internet usage is associated with higher reported breast cancer incidence, as it facilitates awareness and early diagnosis.

Though there is limited direct research on linking all three variables to study associations among internet usage, female employment rates, and breast cancer incidence, I tried to search as much as possible.

For instance, as per NIH report, https://pmc.ncbi.nlm.nih.gov/articles/PMC9408650/ - ref-list1 ,

a study in Taiwan aimed to investigate lifetime breast cancer incidence by different occupational industries among female workers. The study found significant breast cancer risk among the major occupational classifications including manufacturing; wholesale and retail trade; information and communication; financial and insurance activities; real estate activities; professional, scientific and technical activities; public administration; defense; social security; education; and human health and social work activities.

As per BMC Health Services Research, a study conducted in Ghana aimed to contribute to the discourse on factors that influence women’s screening practice by investigating the association between the frequency of internet use and women’s uptake of Clinical breast examination.

It was concluded that Clinical Breast Examination uptake is significantly high among women who frequently use the internet.

Existing studies suggest that occupational factors related to female employment can influence breast cancer risk, with certain job categories and sedentary work associated with higher incidence. Additionally, internet usage plays a significant role in promoting health awareness and screening behaviors, potentially impacting breast cancer incidence and outcomes. However, more research is needed to fully understand the complex interactions between these variables across different countries.