Number of Household Members and Wealth Index Score

Number of Household Members and Wealth Index Score

Summary of the dataset

The data utilized in this case was adopted from the Demographic and health survey, where a total of 6290 respondents were included in the survey. The wealth index score provides a cumulative understanding of a household’s living standards based on the integration of different measures. The different levels of living standards present a different approach, which helps define a different level emphasis under which it is easier to maintain a proper focus on individual development (Chakraborty et al., 2016). Low-income countries tend to have a very low wealthy index compared to high-income countries. This is mainly a result of different factors that present a highly specific consideration on critical aspects such as the average age of household heads, education attainment, and employment status (Duraiappah, 2018). The wealth index encompasses different factors that can be assessed independently to help understand specific concepts that help in building an understanding of individual social and economic development within a given setting. Therefore this research focuses on establishing the underlying factors which influence the wealth index.

Variables used in the analysis

Research variables provide an understanding of different factors that help ensure that the underlying research problem that is being investigated. The variables that are incorporated within the study focuses on both independent and dependent variables. The variables that are included in the study include the wealth index and the number of individuals in the household. Both variables are measured on a continuous scale.

Steps of hypothesis testing

Hypothesis testing presents a primary context where it is possible to understand the underlying research problem, which is essential in defining a successful level of understanding of research outcomes. Hypothesis testing focuses on assessing the underlying research problem in a more organized manner.

Hypothesis

The first step is the identification of an assumption which informs the research development. It is vital to help maintain a positive context within which better processes help achieve a greater focus on the research outcomes. When a researcher focuses on developing a study, different factors are incorporated within the ideology development. A researcher must make an assumption which will help in ensuring that the findings obtained are accurate and help in information dissemination. It is the assumption that helps in building strong research-based on gap identification.

The hypothesis included in the study are

Null hypothesis (Ho): There is no statistically significant relationship between several household members and the wealth index score.

The alternative hypothesis (Ha): There is a statistically significant relationship between the number of household members and the wealth index score.

The level of significance

Assessment of research outcomes presents a greater emphasis on the basis under which the results are considered significant. The study utilizes a 95% confidence interval. The consideration of a 5% error presents a better understanding of the outcome.  A researcher may choose a 90% confidence level or a 99% confidence level. Thus the emphasis on the chance of having accurate outcomes and the error that is included in the study presents a more transparent platform within which it is possible to improve understanding of the assumption that is being developed.

Selection of a test-statistic

The test statistic that is utilized in this case is a coefficient correlation (r). This helps in understanding the relationship between variables while ensuring that there is a more significant consideration of better concepts that influence improved research findings. Correlation analysis presents a better focus on the existing relationship between many individuals in a household and the wealth index score. Correlation analysis gives an understanding of the underlying direction and strength of how the two variables being investigated are linked.

Correlation analysis was the most appropriate statistical test in this case based on the variables that were included. Preliminary assessment of assumptions that must be met when conducting correlation analysis has been effectively achieved. Variables form a better context where it is easier to understand vital statistical methods that are implemented to ensure the successful integration of better statistical systems for change. Identification of variables in research present a better system where it is possible to help determine essential elements that are being investigated within the research.

Rejection region

The rejection region, in this case, is assessed based on a 95% confidence level. Thus, the null hypothesis will be rejected if the p-value is less than 0.05. This will be based on the understanding that there is sufficient evidence to reject the null hypothesis.

Statistical significance provides a focus based on a given level of confidence and error that is assessed in determining better outcomes. The statistical significance is used based on the underlying statistical test. It is integral to focus on providing a strong approach within which it is easier to make a decision. The rejection region, therefore, is assessed based on a p-value analysis to help in defining a unique consideration on important measures that help improve better outcomes. The decision that is made focuses on presenting an understanding of specific elements that help in building a high level of performance.

Results

The findings from correlation analysis show that there is a positive weak, significant relationship between a number of household members and the wealth index factor score (r = 0.081, p= 0.000, p<0.05).

The findings from the analysis provide an understanding of the underlying relationship between the wealth index score and the number of members of the household. The results have shown that the number of individuals with a given household is correlated with the wealth index factor score. This means that an increase in the number of household members is associated with a very small increase in the wealth index score. Therefore it is crucial to understand that the number of household members has a weak influence on the wealth index factor scale. Correlation analysis does not indicate causation. This means that there is a need to understand whether a number of household members can predict the wealth index score.

References

Briones, K. (2017). ‘How many rooms are there in your house?’Constructing the young lives wealth index.

Chakraborty, N. M., Fry, K., Behl, R., & Longfield, K. (2016). Simplified asset indices to measure wealth and equity in health programs: a reliability and validity analysis using survey data from 16 countries. Global Health: Science and Practice, 4(1), 141-154.

Duraiappah, A. (2018). The Inclusive Wealth Index: Measuring the Sustainability of the Sustainable Development Goals. In Ecology, Economy, and Society (pp. 37-48). Springer, Singapore.

Egede, L. E., Voronca, D., Walker, R. J., & Thomas, C. (2017). Rural-Urban Differences in Trends in the Wealth Index in Kenya: 1993-2009. Annals of global health, 83(2), 248-258.