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Introduction to Quantitative Analysis: Testing for Multiple Regression


Walden University RSCH

6210



Introduction to Quantitative Analysis: Testing for Multiple Regression

The following statistical analysis utilized the Afrobarometer Dataset C. The first research question ask

statistically significant relationship between age, lived poverty index, and respondent trust in government. Th

predicts a statistically significant relationship between the predictor variables and outcome variable. The inde

included Q1: Age (n = 14,327; M = 37.17; SD = 14.55) which consists of values ranging from 18 to 100 and L

Index (n = 14,269; M = 1.25; SD = .05) which consists of values ranging from .0 to 4.0. The dependent variab

government (n = 10,953; M = 8.11; SD = 4.23) which consists of values ranging from .00 to 15.00, higher va

trust. All three variable use an interval/ratio level of measurement.

Table 1

Model Summary Statistics

Model R R2 Adjusted R2 Std. Error of the Estimate Durbin-Watson
1 .098a .010 .010 4.20662 2.020 a.

Predictors: (Constant), Q1. Age, Lived Poverty Index (average index of 5 poverty items)
b. Dependent Variable: Trust in Government Index (higher scores=more trust)

, Table 2

ANOVA Statistics

Model Sum of Squares df Mean Square F Sig.
1 Regression 1858.853 2 929.426 52.523 .000b
Residual 189732.560 10722 17.696

Total 191591.412 10724

a. Dependent Variable: Trust in Government Index (higher scores=more trust)

b. Predictors: (Constant), Q1. Age, Lived Poverty Index (average index of 5 poverty items)