1.A study is conducted to determine the factors that affect the increased maternal mortality rates in Kenya. Establish the variables (at least 5 variables) that you would use to conduct the study. State your expected results and explain how you will apply Correlation and Regression analysis in your study. When will you conclude that your results are spurious? (13 marks)
To conduct a study on the factors that affect increased maternal mortality rates in Kenya, the following variables can be considered:
1. Maternal Age: The age of the mother at the time of giving birth.
2. Access to Pre-Natal Care: The availability and utilization of pre-natal healthcare services by pregnant women.
3. Education Level: The educational background of the mother, which can be an indicator of her knowledge and awareness regarding maternal health.
4. Socioeconomic Status: The economic and social standing of the mother, including income level, occupation, and living conditions.
5. Availability of Skilled Birth Attendants: The presence and accessibility of trained healthcare professionals during childbirth.
Expected Results:
The expected results may vary, but it is reasonable to assume that higher maternal mortality rates would be associated with factors such as younger age, limited access to pre-natal care, lower education level, lower socioeconomic status, and lack of skilled birth attendants.
Correlation and Regression Analysis:
Correlation analysis aims to measure the strength and direction of the relationship between variables. In this study, it can be used to determine whether there is a significant correlation between each of the variables mentioned above and maternal mortality rates. For example, the correlation between maternal age and maternal mortality rates can help assess whether younger or older mothers are more at risk.
Regression analysis can be applied to examine how the independent variables (maternal age, access to pre-natal care, education level, socioeconomic status, and availability of skilled birth attendants) collectively affect the dependent variable (maternal mortality rates). It can help identify which variables have a more significant impact on maternal mortality rates, controlling for the influence of other variables.
Concluding Spurious Results:
Spurious results occur when a correlation exists between variables, but they are not causally linked or are influenced by a third variable. To minimize the risk of spurious results, researchers should carefully select variables based on theoretical justifications, collect sufficient data, and ensure statistical significance through appropriate sample sizes and statistical tests. Additionally, controlling for confounding variables and conducting robustness checks can help support or reject the causal relationships between variables.
To conduct a study on the factors that affect increased maternal mortality rates in Kenya, several variables can be considered. Here are at least 5 variables that you can use:
1. Maternal health care access: This variable examines the availability and accessibility of healthcare facilities and services for pregnant women in different regions or communities.
2. Socioeconomic status: This variable investigates the influence of economic factors on maternal mortality rates, such as income levels, education, and employment status.
3. Maternal age: This variable looks at the relationship between the age of pregnant women and the risk of maternal mortality. It can compare outcomes for teenage mothers, women in their reproductive age, and older mothers.
4. Quality of healthcare: This variable assesses the level of healthcare quality, including the presence of skilled healthcare providers, facilities, and equipment for prenatal and delivery care.
5. Cultural beliefs and practices: This variable explores the impact of cultural factors on maternal mortality rates, such as traditional beliefs, practices, and attitudes towards pregnancy and childbirth.
Expected results:
Based on these variables, you can expect to find correlations and regression analysis results that suggest:
- Maternal health care access, socioeconomic status, and quality of healthcare are likely to have a negative correlation with maternal mortality rates. Higher access to healthcare, better socioeconomic conditions, and improved quality of healthcare may lead to lower maternal mortality rates.
- Maternal age may show a U-shaped relationship. Teenage pregnancies and advanced age pregnancies can both be associated with higher maternal mortality rates compared to pregnancies in mid-adulthood.
- Cultural beliefs and practices may show mixed results. Some cultural practices may contribute to increased maternal mortality rates, while others may not have a significant impact.
Correlation and regression analysis:
Correlation analysis can be used to determine if there is a relationship between the variables. It helps identify whether any two variables are positively or negatively correlated, or if there is no correlation at all. For example, you can assess the correlation between maternal health care access and maternal mortality rates.
Regression analysis can then be employed to quantify the relationship between the variables and make predictions. It determines the extent of the impact one variable has on another. For example, you can use regression analysis to measure the effect of socioeconomic status on maternal mortality rates, while controlling for other variables.
Spurious results:
Spurious results occur when two variables appear to be related, but their association is coincidental or caused by a third, unaccounted-for variable. To conclude that results are spurious, you should consider:
- The presence of a common underlying causative factor that affects both the dependent and independent variables but is not included in the analysis.
- The existence of confounding variables that are related to both the independent and dependent variables, leading to a false association.
Therefore, to minimize spurious results, it is essential to carefully select variables, control for confounding factors, and use statistical techniques like regression analysis to evaluate the independent impact of each variable on maternal mortality rates.