Discussion week 9
Introduction
A collection of statistical methods for estimating relationships between a dependent variable and
one or more independent variables is known as regression analysis. It can be used to determine
the strength of a relationship between variables and to predict how they will interact in the
future.
Variables
Afrobarometer data was utilized to complete this assignment. The mean age from the
information given was 37.19 ± 0.65 years. Two independent variables (age and gender) and one
dependent variable (current level of democracy) were chosen. A regression analysis was done to
establish the relationship between the independent and dependent variables.
Ordinal scale was used to measure the independent variable (age) while interval ratio scale was
used to measure the dependent variable (current level of democracy).
The research question for this research was; what is the influence of age, gender on level on their
country’s current level of democracy? A study hypothesis was formulated from this research
question as follows;
H1: there is a significant relationship between the respondents’ perception on their country’s
current level of democracy and their age and gender.
H0: there is no significant relationship between the respondents’ perception on their country’s
current level of democracy and their age and gender.
Findings
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1.011a.000 .000 1.994
a. Predictors: (Constant), Q101. Gender of respondent, Q1.
Age
The multiple regression coefficient of the model is shown by R value. The value, 0.011 measures
the quality of the model prediction in reference to respondents perception on the dependent
variable (current level of democracy). The value shows a low or poor level of prediction. In
reference to Bruce and Peter, (2017) on a scatterplot, the correlation coefficient r represents the
intensity and direction of a linear relationship between two variables. The coefficient of (R2 =
0.000) shows the proportion of variance in the level of democracy for which the research can
find explanation from age and gender.
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 4.590 2 2.295 .577 .000b
Residual 36447.104 9168 3.975
Total 36451.694 9170
a. Dependent Variable: Q46d. Level of democracy: in the future (0-10 scale)
b. Predictors: (Constant), Q101. Gender of respondent, Q1. Age
The ANOVA table above indicates whether the regression model is fit for data results. The
findings shows that age and gender (independent variables) significantly predicts the outcome of
perception of level of democracy, (p < 0.05 = 0.000).
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 9.108 .089 102.525 .000
Q1. Age .002 .001 .011 1.050 .002
Q101. Gender of
respondent
-.014 .042 -.003 -.331 .741
a. Dependent Variable: Q46d. Level of democracy: in the future (0-10 scale)
The respondents’ age as indicated by (t=1.050, p<0.005=.002) were significant while gender was
insignificant at (t= -.331, p>0.005=0.741).
This shows that the explanatory variables gender is no more useful in the model, when the
remaining variables are already included in the model.
The general model
Level of democracy = 9.108-0.02(Gender) + 0.002(age)
The null hypothesis is rejected in favor of the alternative hypothesis based on the results of the
model's ANOVA table, as there is a statistically significant association between the respondents'
views on their country's current level of democracy and their age and gender.
References
Bruce, Peter, and Andrew Bruce. 2017.Practical Statistics for Data Scientists. O’Reilly Media.
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th
ed.). Thousand Oaks, CA: Sage Publications.
