Age & Socioeconomic Factors: A Study of Relationships Paper

Research Question

What is the relationship between Lived Poverty Index, Handling of Economy Index, and Level of Democracy: Today, as they relate to Infrastructure Index?

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Null Hypothesis

There is no statistically significant relationship between Lived Poverty Index, Handling of Economy Index, and Level of Democracy: Today, as they relate to Infrastructure Index?

The Most Appropriate Research Design

The most appropriate research design that can be used to address the above research question is the retrospective study design. In retrospective studies, the outcome of interest (dependent variable) has already occurred at the time the research is conducted (Salkind, 2010b). For example, in Infrastructure Index (the outcome variable), the infrastructure (e.g., road and rail networks) is already in place. That is, the respondents are already aware of the current status of their countries’ infrastructure. Therefore, the respondents can indicate their perception of the quality of the infrastructure based on their experiences. In retrospective studies, research utilizes already existing databases.

In a retrospective study, the researcher documents a naturally occurring relationship between the independent variable an outcome s/he is investigating. That is, the investigator does not manipulate the predictor variables or carry out any intervention to establish its effect on the outcome. This is because the outcome of interest in a study has already occurred naturally or by some other factor. It is worth noting that the retrospective study design is the most relevant for addressing the current multiple regression analysis questions because the predictor variables cannot be manipulated for practical and ethical reasons.

The Dependent Variable and its Measurement

The dependent variable that was chosen for this assignment is Infrastructure Index. It is measured at the continuous level of measurement.

Independent Variable and its Measurement

The independent variables selected for the current multiple regression analysis include Lived Poverty Index, Handling of Economy Index, and Level of Democracy: Today. All of them are measured at a continuous level of measurement.

Other Variables Added to The Multiple Regression Models as Controls

The controls included in the multiple regression model include Trust in Government Index and Age.

Justification for Adding the Control Variables

The term control is used to describe a variable that is not of vital interest (i.e., neither the predictor nor the outcome of interest) and thus an extraneous factor whose effect on the dependent variable needs to be eliminated or controlled (Salkind, 2010a). The term is also used to explain the researcher’s need to estimate and effect of interest (association) that is independent of the influence of the extraneous factors. That is, adding control in the regression model, the investigator ensures that any bias that may influence the outcome is controlled.

Trust in government was added as a control because it affects the success of many public policies that depend on behavioral responses from the citizens (OECD, 2020). For example, public trust is positively associated with enhanced adherence to regulations and tax regime. Trust is also vital because it enhances an investor’s confidence. Therefore, if there is no public trust in the government, there is a decreased likelihood that governments will receive funding from development partners and will also not attract investors. For example, with increased public trust in government, there is more likelihood that development partners will invest great infrastructure projects. However, in the absence of trust, this will not be realized. Therefore, trust in government was added because any change in this variable invalidates the relationship between the predictors and the outcome.

Age is also added as a control variable because it was highly likely to affect the association between the predictors and the outcome. For example, the younger population (e.g., young adults) are less likely to give an objective assessment of infrastructure.

Significance and Strength of Effect

In a multiple regression model, effect size indicates the strength of the relationship between the predictor and outcome variable, which is measured using r. In the current analysis, r = 0.618 showing that the effect size is strong. This implies that the predictors (Lived Poverty Index, Handling of Economy Index, and Level of Democracy: Today) predicts 61.8% of the variation in the outcome (Infrastructure Index). Upon adding two control variables (Trust in Government Index and Age), the strength of the effect becomes stronger (r = .631). This means that This implies that when controls are added, the predictors (Lived Poverty Index, Handling of Economy Index, and Level of Democracy: Today) predicts 63.1% of the variation in the outcome (Infrastructure Index).

Explanation of Results to Lay Audience and Answering of Research Question

The Model Summary shows the results of regression analysis. It can be seen that the multiple determination for Model 1 is .618. Simply, it can be concluded that 61.8% of the variation in respondents’ Infrastructure Index score is explained by Lived Poverty Index, Handling of Economy Index, and Level of Democracy: Today. This implies that a combination of the three predictor variables (Lived Poverty Index, Handling of Economy Index, and Level of Democracy: Today) explain 61.8% of the outcome variable (Infrastructure Index score).

For the final model (Model 2), the coefficient of multiple determination is .631. Therefore, it can be concluded that 63.1% of the variation in respondents’ Infrastructure Index score is explained by Lived Poverty Index, Handling of Economy Index, Level of Democracy: Today, Age, and Trust in Government Index. This implies that the new variables added in model 2 (Age and Trust in Government Index) account for 1.3% of the variation in Infrastructure Index.

Conclusion

In conclusion, the answer to the research question is that Lived Poverty Index, Handling of Economy Index, and Level of Democracy: Today are moderately strong predictors of Infrastructure Index. If the public has a high level of satisfaction in the handling of the economy, believes that a country is democratic, and perceives a nation as having low levels of poverty, there is a high likelihood that s/he will recognize that the country has excellent infrastructure.

References

OECD. (2020). Trust in government. https://www.oecd.org/gov/trust-in-government.htm

Salkind, N. J. (2010a). Control variables. Encyclopedia of Research Design. https://doi.org/10.4135/9781412961288.n77

Salkind, N. J. (2010b). Retrospective Study. https://methods.sagepub.com/reference/encyc-of-research-design/n390.xml