Assignment: Testing for Correlation and Bivariate Regression
Quantitative Reasoning and Analysis
Walden University
Students name
2021
Afro barometer Dataset
To evaluate the correlation and bivariate regression between the two variables, variables from the
Afro-barometer data set were chosen. The above test is used to examine the relationship between
the two variables, which are age and live poverty. The aim of this study is to look into a
significant relationship (Wagner, 2020). Correlations are useful because they enable you to
predict future actions by determining what relationship variables exist. In the social sciences,
such as government and healthcare, knowing what the future holds is critical (Nachmias & Leon-
Guerrero, 2018).2
Independent variable (IV)
Age connotes the independent variable and measured on ratio scale of measurement
Dependent variable (DV)
Lived poverty connotes the dependent variable and measured on ratio scale of measurement.
Ratio scales have the same quantitative significance since the variations between the scale values
are identical. A true zero point exists on ratio scales. A true zero point on the scale denotes that
the quantity of the construct being evaluated is zero.
Research design
The correlational research design is suitable for this analysis because the researcher does not
attempt to manipulate the variables; rather, they are measured and relationships between the
dependent and independent variables are sought. (Frankfort-Nachmias and Leon-Guerrero,
2018).
Research questions:
i. What is the relationship between age and lived poverty index
ii. Does age predict lived poverty index
Null hypothesis
i. (HO1) There is no relationship between age and lived poverty index
ii. (HO2) The age of respondents does not predict lived poverty index
Correlations
Q1. Age
Q2. Lived
Poverty.
Q1. Age Pearson
Correlation
1 0.44
N51142 50475
Q2. Lived poverty Pearson
Correlation
0.044 1
N5042 50923
Regression
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1.044a.002 .002 .9436
a. Predictors: (Constant), Lived Poverty
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 86.314 1 86.314 98.057 .000b
Residual 4465.402 50692 0.89
Total 45032.723 50567
a. Dependent Variable: Q1. Age
b. Predictors: (Constant), Lived Poverty
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 1.136 0.013 98.5 .000
Lived Poverty Index
(average index of 5
poverty items)
0.003 00.004 9.4 .000
a. Dependent Variable: Q1. Age
Explanation & Assumption
This afro-barometer analysis uses a correlational research design, which is suitable for this type
of study since the researchers are only interested in determining the relationships between
dependent and independent variables. The dependent variable in this study is "lived poverty,"
while the independent variable is "age." Both variables of age and lived poverty are shown in the
Pearson correlation coefficient performance table (Nachmias & Leon-Guerrero, 2018). This
shows that the value of r=0.044 is greater than p0.05, indicating that the association is important.
We can infer that there is a poor association between the variables based on the same value of r =
0.044. We can infer that there is a poor association between the variables of age and lived
poverty based on the same value of r = 0.044. (Alessandro, 2018). The table of ANOVA value of
F=98.067 is also higher than the p-value of 0.05, indicating that the model is important.
Social Change implication
Since our p-value is less than.05 (alpha) and the r=.044, we can conclude that between age and
the live poverty index, there is a small positive linear relationship or almost no linear
relationship. Again, we can argue that the key cause of poverty is not age, but rather shifting
economic conditions, a lack of schooling, a high divorce rate, a culture of poverty,
overpopulation, epidemic diseases such as AIDS and malaria, and environmental problems such
as lack of rainfall (Alessandro, 2018). In many nations, extreme weather can be a source of
poverty. Drought, rainfall, and flooding are some of the most common weather-related causes of
poverty. When natural disasters do not cause significant damage. When natural disasters go
unnoticed by the public, it becomes more difficult to raise funds. This is exacerbated when
governments invest money in the capitals rather than in the poorest regions, where it is most
needed (Alessandro, 2018).
References
Alessandro, B. (2018, feb). How to Describe Bivariate Data. Journal of Thoracic Disease.
Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5864614/
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th
ed.). Thousand Oaks, CA: Sage Publications.
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social
science statistics (7th ed.). Thousand Oaks, CA: Sage Publications
