The First Independent Variable: What You Need to Know

Describing Variables 

The first independent variable (IV1) is    respondent’s highest degree , and its level of measurement is ordinal.  The second independent variable (IV2) is    respondent’s sex , and its level of measurement is nominal.  The dependent variable (DV) is respondent’s income in constant dollars   and is at the ratio level of measurement.  

Research Question and Hypotheses 

The research question (RQ) for this study would be:    What is the relationship between respondent’s highest degree and respondent’s sex, and the respondent’s income in consistent dollars?     The research (or alternate hypothesis) would be:    There is a relationship between respondent’s highest degree and respondent’s sex, and the respondent’s income in consistent dollars.     The null hypothesis would be:    There is no relationship between respondent’s highest degree and respondent’s sex, and the respondent’s income in consistent dollars. 

Research Design 

The research design for this study would be correlational, as we are examining information gathered from surveys to determine relationships between variables, without employing or manipulating interventions (Laureate, 2048a, p. 59).  We will be using multiple regression to examine these relationships, as it is the most appropriate technique to both appreciate the entire model (how the variables interact), and understand the contributions of each variable (Pallant, 2016, p. 149).  

Data 

Descriptive Statistics
	Mean	Std. Deviation	N
RESPONDENTS INCOME	10.38	2.914	1523
RS HIGHEST DEGREE	1.81	1.228	1523
RESPONDENTS SEX	1.51	.500	1523

Figure 1.   Variables used for the multiple regression analysis: IV1 respondent’s highest degree, IV2 respondent’s sex, and DV respondent income in constant dollars (General Social Survey Study, 2009).       

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.268 a	.072	.071	2.809
a. Predictors: (Constant), RESPONDENTS SEX, RS HIGHEST DEGREE

Figure 2.   Model summary, demonstrating a combined 7.2% variance (R square) associated with the IV1 and IV2 on the DV (respondent income in constant dollars) (General Social Survey Study, 2009).     

ANOVA a
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	930.003	2	465.002	58.939	.000 b
	Residual	11992.060	1520	7.890
	Total	12922.063	1522
a. Dependent Variable: RESPONDENTS INCOME
b. Predictors: (Constant), RESPONDENTS SEX, RS HIGHEST DEGREE

Figure 3.   Analysis of variance between the variables, revealing the multiple regression analysis yielded statistically significant results (Sig. value, or    p- value, = .000, with .05 alpha cut-off) (General Social Survey Study, 2009).     

Coefficients a
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	10.318	.250		41.280	.000
	RS HIGHEST DEGREE	.584	.059	.246	9.955	.000
	RESPONDENTS SEX	-.658	.144	-.113	-4.572	.000
a. Dependent Variable: RESPONDENTS INCOME

Figure 4.   Multiple regression analysis results, demonstrating Beta (regression coefficient) results and associated Sig. values ( p- values) (General Social Survey Study, 2009).           

Multiple Regression Analysis Results 

The Model 

The value of R (correlation coefficient) in Figure 2 is .268 and represents a small effect size between the combined IVs on the DV (Laureate, 2018b, p. 72).  The value of R square (.072) in Figure 2 represents how the two IVs represent a combined 7.2% variance in the value of DV (respondent income in constant dollars) (Pallant, 2016, p. 162).   

The analysis of variance (ANOVA) results (Figure 3) reveal a Sig value ( p- value) of .000 (with .05 alpha cut-off); this means that the combined IVs effect on the DV (the model as a whole) yield statistically significant results, and we must reject the null hypothesis in favor of the research hypothesis: There is a relationship between respondent’s highest degree and respondent’s sex, and the respondent’s income in consistent dollars.   

Independent Variables 

Figure 4 represents our multiple regression analysis results.  The column labelled,    standardized coefficients Beta   displays the regression coefficient (Beta value) when the values of each IV have been “converted to the same scale” for more accurate comparison (Pallant, 2016, p. 162).  These results reveal that IV2 (respondent’s sex) (-.658) contributed more than IV1 (respondent’s highest degree) (.584) to the effect on the DV (respondent income in constant dollars) (Pallant, 2016, p. 162; Laureate, 2018, p. 75).  Another way to say this is that respondent’s sex is a higher predictor of respondent income, than respondent’s the degree achieved in their education.  

Further, each of these regression coefficient results has an associated Sig. value (or    p- value) of .000 (below the .005 Alpha cut-off) and indicates each IV has made a “significant unique contribution to the prediction of the dependent variable” (Pallant, 2016, p. 163).  

Regression Equation 

The regression equation is: respondent income in constant dollars = 10.318 + respondent’s highest degree (0.584) + respondent’s sex (-0.658).  

References 

General Social Survey Study. (2009).   [Data set]. Retrieved from https://class.waldenu.edu 

Laureate (2018a). Skill builder:    Research design and statistical design.   Retrieved from https://class.waldenu.edu 

Laureate (2018b).   Skill builder:    Interpreting correlational and regression coefficients.   Retrieved from https://class.waldenu.edu 

Laureate (2018c). Skill builder:    Interpreting the results from multiple regression models introduction.   Retrieved from https://class.waldenu.edu 

Pallant, J. (2016).    SPSS survival manual: A step by step guide to data analysis using IBM SPSS   (6th ed.). New York, NY: McGraw Hill Education.