The variables would be:
The students' academic performance
Socio-economic of parent's income, based on their residence, occupation and educational experiences.
This week you will perform a basic linear regression.
Please be aware of the very strict data requirement for running linear regression: your DV and IV both have to be continuous variables.
(Most variables at interval/ratio level are continuous variables.) This rule is solid for DV: if your DV is a nominal or ordinal variable, you CANNOT use it as the DV for regression, not even when it is converted to a dummy variable (b/c the regression is no longer linear). It is a necessity that your DV is a "continuous" variable with interval/ratio level of measurement.
If your current DV won't work for regression test, please choose a "continuous" variable from GSS data set as your temporary DV for the week in order to practice regression analysis.
Some examples of "continuous" variables from GSS 2012 data: tvhours, hrs1, etc. You don't have to include regression test in your final portfolio if your DV won't work for regressing test. Keep in mind, regression is also a form of significance test. Your porfolio only needs ONE significance test (we have learned: independent sample t-test, dependent sample t-test, Chi-square, and regression.)
IV: male dummy variable (based on variable "sex") and white dummy variable (based on variable "race")
See equation below. We use * to mark the variable that is statistically significant.
Creating dummy variables
If an IV is not continuous (like race, sex), you could make things work by creating dummy variables based on these variables. For example, based on variable "sex," we can make a "male dummy variable" or a "female dummy variable."
Based on variable "race," we can create a "white dummy variable" or "black dummy variable," or "other dummy variable."
By custom, we'll name the dummy variable using the value we coded as 1. For example, if we denote "male" as 1, female as 0, we'll name this dummy as "male dummy variable."
If we denote "white" as 1, then we'll name this dummy as "white dummy variable." This naming method helps readers/researchers remember/understand what dummy variables stand for in a study.
Here is a youtube video which shows the essential steps of creating dummy variables
In this week's forum discussion, you are required to run a linear regression using:
1. your DV (if your DV is not a continuous variable, pick one from the GSS 2012 data set as your temporary DV for the week so you can practice regression)
2. and two dummy variables created based on variable "sex" and "race" in the GSS 2012 data set.
SPSS command to run linear regression
Analyze - Regression - Linear
Output interpretation
The proper way to interpret linear regression is writing the regression equation.
Here is an example:
DV: educ (highest year of school completed, a continuous variable at I/R level)
IV: male dummy variable (based on variable "sex") and white dummy variable (based on variable "race")
See equation below. We use * to mark the variable that is statistically significant.
Educ=13.031-.054male+.696white*
Here is the fun part: prediction of respondents' highest year of school completed based on their race and sex.
Based on this equation:
A white male by average will have 13.673 years of education: 13.031-.054*1+.696*1=13.673
A nonwhite female by average will have 13.031 years of education: 13.031-.054*0+.696*0=13.031